Mason Street Transportation Corridor Master Plan

Mason StreetTransportation CorridorMaster Plan


October 2000



TRANSPORTATION CORRIDOR

Mason Street

MULTI-MODAL TRAVELDEMAND MODEL



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Mason Street Transportation Corridor Muti-modal Travel Demand Model i

ii Mason Street Transportation Corridor Muti-modal Travel Demand Model

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AcknowledgmentsThe following individuals are recognized for their contribution to thedevelopment of the Mason Street Multi-modal Travel Model and prepa-ration of this report:

CITY OF FORCITY OF FORCITY OF FORCITY OF FORCITY OF FORT COLLINST COLLINST COLLINST COLLINST COLLINS

Susanne Durkin-SchindlerNathan AllenJohn DaggettMark Jackson

CONSULCONSULCONSULCONSULCONSULTTTTTANT TEAMANT TEAMANT TEAMANT TEAMANT TEAM

LSA AssociatesLSA AssociatesLSA AssociatesLSA AssociatesLSA Associates

Ray MoeEverett BaconShelly BrunoJenny Rodgers

PPPPParsons Tarsons Tarsons Tarsons Tarsons Transportation Groupransportation Groupransportation Groupransportation Groupransportation Group

David Kurth

NFRNFRNFRNFRNFRT&AT&AT&AT&AT&AQPC COORDINAQPC COORDINAQPC COORDINAQPC COORDINAQPC COORDINATIONTIONTIONTIONTION

Suzette ThiemanBob Hazlett, Jr.

ACKNOWLEDGMENTS

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Table of ContentsIntroduction ........................................................................................................................................................................................................................................... 1

Data Collection and Analysis .................................................................................................................................................................................................................. 41999 Fort Collins Transportation Surveys ............................................................................................................................................................................................................ 4

Colorado State University Special Generator Study ...................................................................................................................................................................................... 4Mason Street Transportation Corridor Vehicle Intercept Survey .................................................................................................................................................................. 5Transfort Onboard Transit Survey ................................................................................................................................................................................................................ 5

1998 Mobility Report Card Household Survey ...................................................................................................................................................................................................... 5Traffic Counts ...................................................................................................................................................................................................................................................... 7

Socioeconomic Data/Traffic Analysis Zone Structure ............................................................................................................................................................................. 8Development of 1998 and 2020 Socioeconomic Data ........................................................................................................................................................................................... 8

1998 Socioeconomic Data ............................................................................................................................................................................................................................ 82020 Socioeconomic Data ............................................................................................................................................................................................................................ 9University of Northern Colorado ................................................................................................................................................................................................................... 9

Adjustments to the Traffic Analysis Zone Structure ............................................................................................................................................................................................ 12

Networks ............................................................................................................................................................................................................................................. 13Roadway Networks ............................................................................................................................................................................................................................................ 13

1995 Roadway Network ............................................................................................................................................................................................................................. 131998 Roadway Network ............................................................................................................................................................................................................................. 132020 Roadway Network ............................................................................................................................................................................................................................. 15

Transit Networks ............................................................................................................................................................................................................................................... 15Transit Line Coding ..................................................................................................................................................................................................................................... 16Park and Ride Lot Coding ........................................................................................................................................................................................................................... 17Walk and Drive Access Coding ................................................................................................................................................................................................................... 18

Trip Generation .................................................................................................................................................................................................................................... 19Estimation of Zonal Household Size and Income Distributions ........................................................................................................................................................................... 19New Trip Purposes ............................................................................................................................................................................................................................................ 20

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Trip Production Estimation and Allocation Models .............................................................................................................................................................................................. 21Production Allocation Model for Home-Based University Trips ................................................................................................................................................................... 22Work-Based Other Production Allocation Model ......................................................................................................................................................................................... 23

Trip Attraction Estimation and Allocation Models ............................................................................................................................................................................................... 23Special Generators ............................................................................................................................................................................................................................................ 24Internal/External Trips ...................................................................................................................................................................................................................................... 25Balancing Productions and Attractions .............................................................................................................................................................................................................. 26

Pathbuilding ......................................................................................................................................................................................................................................... 28Roadway Pathbuilding ....................................................................................................................................................................................................................................... 28Transit Pathbuilding ........................................................................................................................................................................................................................................... 29

Trip Distribution ................................................................................................................................................................................................................................... 31

Mode Choice ........................................................................................................................................................................................................................................ 33Overview ........................................................................................................................................................................................................................................................... 33Non-Motorized Mode Split ................................................................................................................................................................................................................................. 33Motorized Mode Choice ..................................................................................................................................................................................................................................... 35

Home-Based Work Model Specification ...................................................................................................................................................................................................... 38Home-Based University Model Specification ............................................................................................................................................................................................... 41Non-Work Mode Choice Models ................................................................................................................................................................................................................. 42Home-Based Shopping and Home-Based Other Auto Occupancy Models .................................................................................................................................................... 42Work-Based Other Auto Occupancy Models ............................................................................................................................................................................................... 43Other-Based Other Auto Occupancy Models ............................................................................................................................................................................................... 43

Time-of-Day Traffic Assignment ........................................................................................................................................................................................................... 44Time-of-Day Analysis ......................................................................................................................................................................................................................................... 44

Determination of Time Periods ................................................................................................................................................................................................................... 44Time-of-Day Factors by Trip Purpose ......................................................................................................................................................................................................... 45

Traffic Assignment ............................................................................................................................................................................................................................................. 47

Speed Estimation and Feedback ........................................................................................................................................................................................................... 491998 Observed Speeds ...................................................................................................................................................................................................................................... 49

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Speed Estimation ............................................................................................................................................................................................................................................... 50Speed Feedback ................................................................................................................................................................................................................................................ 50

1998 Model Validation .......................................................................................................................................................................................................................... 52Vehicle Miles of Travel ....................................................................................................................................................................................................................................... 52Screenlines ........................................................................................................................................................................................................................................................ 53

Performance Module ........................................................................................................................................................................................................................... 55File Name Summary .......................................................................................................................................................................................................................................... 55Socioeconomic Data for the Region ................................................................................................................................................................................................................... 55Socioeconomic Data for Fort Collins .................................................................................................................................................................................................................. 55Trip Generation for the Region .......................................................................................................................................................................................................................... 55Trip Generation for Fort Collins .......................................................................................................................................................................................................................... 55Trip Distribution ................................................................................................................................................................................................................................................. 55Bike and Pedestrian Trips for the Region ........................................................................................................................................................................................................... 55Bike and Pedestrian Trips for Fort Collins .......................................................................................................................................................................................................... 56Mode Split and Mode Choice for the Region ....................................................................................................................................................................................................... 56Vehicle Trips Assigned for the Region ................................................................................................................................................................................................................ 56Validation and Screenlines for the Region .......................................................................................................................................................................................................... 56VMT Summary for the Region ............................................................................................................................................................................................................................ 56VMT Summary for Fort Collins ........................................................................................................................................................................................................................... 56Speeds, VHT, and Congestion for the Region ..................................................................................................................................................................................................... 56Emissions for the Region ................................................................................................................................................................................................................................... 56Emissions for Fort Collins .................................................................................................................................................................................................................................. 57

Scenario Manager ................................................................................................................................................................................................................................ 58Scenario Manager Files ..................................................................................................................................................................................................................................... 58

Mason Model Users Guide ................................................................................................................................................................................................................... 59Getting the Data ................................................................................................................................................................................................................................................ 59Loading the Mason Street Model Add-In ............................................................................................................................................................................................................ 59Loading the Performance Module Add-In ........................................................................................................................................................................................................... 59Loading the Alternatives .................................................................................................................................................................................................................................... 60

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Setting Up the Scenarios ................................................................................................................................................................................................................................... 61Running a Scenario ............................................................................................................................................................................................................................................ 61Running the Performance Module ...................................................................................................................................................................................................................... 62Creating Transit Network Files .......................................................................................................................................................................................................................... 62Modifying the Roadway Network ....................................................................................................................................................................................................................... 63

FiguresFigure 1: Mason Street Multi-Modal Travel Demand Model Flow Diagram ............................................................................................................................................ 1Figure 2: Mason Street Model Phasing ................................................................................................................................................................................................ 2Figure 3: 1998 Household Density .................................................................................................................................................................................................... 10Figure 4: 2020 Household Density .................................................................................................................................................................................................... 10Figure 5: 1998 Employment Density .................................................................................................................................................................................................. 11Figure 6: 2020 Employment Density .................................................................................................................................................................................................. 11Figure 7: 1998 Roadway Network ..................................................................................................................................................................................................... 14Figure 8: 1998 Transit Route Structure on Roadway Network ............................................................................................................................................................. 16Figure 9: Park and Ride Coding ......................................................................................................................................................................................................... 17Figure 10: 1998 CSU Productions ..................................................................................................................................................................................................... 22Figure 11: 1998 UNC Productions .................................................................................................................................................................................................... 23Figure 12: Friction Factor Curves ....................................................................................................................................................................................................... 32Figure 13: Multinomial Logit Structure .............................................................................................................................................................................................. 36Figure 14: Nested Logit Structure ...................................................................................................................................................................................................... 37Figure 15: Diurnal Distribution for 1998 MRC Weighted and Expanded Vehicle Trips .......................................................................................................................... 45Figure 16: Regional Screenlines ........................................................................................................................................................................................................ 53

TablesTable 1: 1998 Mobility Report Card Expansion Factors ......................................................................................................................................................................... 6Table 2: 1998 Mobility Report Card Weighted and Expanded Regional Households .............................................................................................................................. 6Table 3: 1998 Mobility Report Card Weighted and Expanded Trips ....................................................................................................................................................... 6Table 4: Truck Factors for Adjustment of Raw Traffic Counts ................................................................................................................................................................. 7Table 5: 1998 and 2020 Household Data ........................................................................................................................................................................................... 9Table 6: 1998 and 2020 Employment Data ......................................................................................................................................................................................... 9Table 7: Adjustments to the TAZ Structure ......................................................................................................................................................................................... 12Table 8: Input Free-flow Speeds (mph) ............................................................................................................................................................................................... 14Table 9: Input Congested Speeds (mph) ............................................................................................................................................................................................ 14Table 10: Level-of-Service C Roadway Capacities (vehicles per hour per lane) ..................................................................................................................................... 15Table 11: Roadway Network Centerline Miles .................................................................................................................................................................................... 15Table 12: Roadway Network Lane Miles ............................................................................................................................................................................................ 15Table 13: Modeled and Scheduled Bus Travel Times .......................................................................................................................................................................... 17Table 14: 1998 Households by Size and Income Groups .................................................................................................................................................................... 20Table 15: 2020 Households by Size and Income Groups .................................................................................................................................................................... 20Table 16: Trip Production Rates ......................................................................................................................................................................................................... 21Table 17: Work-Based Other Production Allocation Model .................................................................................................................................................................. 23Table 18: Trip Attraction Rates .......................................................................................................................................................................................................... 24Table 19: 1998 Special Generator Values in the Mason Street Model ................................................................................................................................................. 25

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Table 20: 2020 Special Generator Values in the Mason Street Model ................................................................................................................................................. 25Table 21: Jurisdictional Trip Rate Factors for Internal/External Trips ..................................................................................................................................................... 26Table 22: Internal/External Trips in the Mason Street Travel Model ...................................................................................................................................................... 26Table 23: 1998 Productions and Attractions ...................................................................................................................................................................................... 27Table 24: 2020 Productions and Attractions ...................................................................................................................................................................................... 27Table 25: Productions per Household ................................................................................................................................................................................................ 27Table 26: Terminal Penalties (impedance units) .................................................................................................................................................................................. 29Table 27: Average Transit Fares by Purpose ....................................................................................................................................................................................... 29Table 28: Transit Pathbuilding Weights and Parameters ...................................................................................................................................................................... 29Table 29: Assigned and Observed Boardings by Route ....................................................................................................................................................................... 30Table 30: Average Trip Length (miles) ................................................................................................................................................................................................ 32Table 31: Average Trip Length (minutes) ............................................................................................................................................................................................ 32Table 32: Friction Factor Parameters .................................................................................................................................................................................................. 32Table 33: Non-motorized Mode Share Models for All Zones Except University Zones .......................................................................................................................... 34Table 34: Non-motorized Mode Share Models for University Zones .................................................................................................................................................... 34Table 35: Observed and Modeled Non-Motorized Mode Shares for 1998 ........................................................................................................................................... 35Table 36: Home-Based Work Mode Choice Model Coefficients ........................................................................................................................................................... 40Table 37: Example Walk Access / Egress Markets ............................................................................................................................................................................... 40Table 38: Home-Based University Mode Choice Model Coefficients .................................................................................................................................................... 41Table 39: Non-Work Mode Choice Model Coefficients ........................................................................................................................................................................ 42Table 40: 1998 MRC Weighted Expanded Vehicle Trips ...................................................................................................................................................................... 45Table 41: Time-of-Day Directional Factors ......................................................................................................................................................................................... 47Table 42: BPR Parameters ................................................................................................................................................................................................................. 48Table 43: Estimated Free-flow (Off-Peak) Speeds (mph) ..................................................................................................................................................................... 50Table 44: Estimated Congested (Peak) Speeds (mph) ......................................................................................................................................................................... 50Table 45: BPR Parameters for Speed Estimation ................................................................................................................................................................................. 50Table 46: 1998 Final Speeds after Feedback ...................................................................................................................................................................................... 51Table 47: 2020 Final Speeds after Feedback ...................................................................................................................................................................................... 51Table 48: 1998 VMT by Functional Type and Jurisdiction ................................................................................................................................................................... 52Table 49: 1998 VMT by Jurisdiction .................................................................................................................................................................................................. 52Table 50: 1998 Screenline Analysis ................................................................................................................................................................................................... 53Table 51: Network Fields ................................................................................................................................................................................................................... 64

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Mason Street Transportation Corridor Muti-modal Travel Demand Model 1

In January 1999, the City of Fort Collins initiated the Mason Street Transportation

Corridor project. It is intended to enhance opportunities for pedestrians, bicyclists,

and transit riders along its length, to encourage in-fill development, and to provide

for economic opportunities. It will provide a direct north/south route for bicyclists

and pedestrians and potentially serve as a transit corridor for those travelers wanting

to avoid the use of autos on the congested College Avenue corridor. It also offers the

future possibility of rail service from Fort Collins to destinations throughout the Front

Range region. In 1997, Fort Collins voters approved Building Community Choices,

which included Mason Street improvements from Cherry Street on the north to Harmony

Road on the south, centered along the Burlington-Northern railroad tracks.

To support efforts on the Mason Street Transportation Corridor project and other

planning activities in the City, a multi-modal travel demand forecasting capability

was desired. The existing regional travel model maintained by the North Front

Range (NFR) Transportation and Air Quality Planning Council, the region’s

metropolitan planning organization (MPO), did not have a multi-modal capability

that could provide for the analysis of transit options. Furthermore, the existing

model was written using MinUTP software, a DOS-based platform that is limited in

its graphical and computer processing abilities.

As a result, the NFR MinUTP model was converted to the Windows-based TransCAD

software by Caliper Corporation in the fall of 1999. A detailed comparison of the

1995 TransCAD-converted model and the original 1995 MinUTP model was

conducted and documented in the November 1999 technical report, Mason Street

Multi-modal Travel Demand Model, Phase I: Model Conversion and Replication—

Preliminary Analysis.

IntroductionFigure 1: Mason Street Multi-Modal Travel Demand Model Flow Diagram

INTRODUCTION

2 Mason Street Transportation Corridor Muti-modal Travel Demand Model

Phase I: Model Conversion and Replication

Socioeconomic Data/TAZ Structure• Disaggregate Fort Collins zones and socioeconomic data• Develop GIS-based TAZ structure• Fix negative trends in Fort Collins socioeconomic data• Disaggregate zones in Berthoud

Network/Pathbuilding• Conduct detailed Fort Collins network review• Conflate region-wide network to actual distances,• Update facility and area types and corresponding speeds and capacities region-wide• Correct ramp coding on 1-25 (conversion error)• Correct terminal penalty inconsistencies and apply to both trip ends (conversion

error)• Correct pathbuilding process to minimize impedance (conversion error)• Connect all zones to network• Develop 1998 and 2020 TransCAD roadway networks from 1995

Trip Distribution•Fix K-factoring to match MINUTP model (conversion error)

Mode Split•Correct calculation of bike/pedestrian shares (MinUTP error)•Add external/external trips (conversion error)

Traffic Assignment•Fix process to minimize impedance (conversion error)•Fix coding of volume-delay equation variables (conversion error)

Performance Reporting•Develop basic performance reporting routines

Validation•Review and update traffic count file; adjust for trucks

Phase II: Multi-Modal Enhancements

Networks/Pathbuilding•Develop peak and off-peak transit networks for 1998 and 2000•Code park and ride lots•Develop transit pathbuilding routine•Develop walk and drive access links

Although it was the 1995 model that was converted and compared in the preliminary

analysis, the resulting TransCAD base year Mason Street Transportation Corridor

model was produced for 1998. The 1998 model was developed through updates to

the 1995 model; and subsequently, the 2020 model was derived from 1998. The

basic model structure and general parameters are the same for all model years. The

four-step modeling process and its supporting structure are shown in Figure 1.

The preliminary analysis set the stage for full redevelopment of the regional model

with emphasis on Fort Collins and the Mason Street Transportation Corridor. In

order to support the schedule for the Mason Street Transportation Corridor project,

the model enhancements were conducted in phases. Phase I included the preliminary

analysis and the conversion and replication of the original MinUTP model in

TransCAD. Exact replication was never attained because concerns with the model’s

performance and operation were addressed during the process. The multi-modal

(i.e., transit) capability was added in Phase II through the provision of a mode

choice model and the associated upgrades necessary to support it. Finally, in Phase

III recent travel behavior information was incorporated into the model along with

several enhancements that bring the model up to a reasonable state-of-the-practice

for a region with the size and character of the North Front Range. Phase III

enhancements essentially amounted to a complete model overhaul. The specific

improvements for each phase are summarized in Figure 2.

This technical report describes each enhancement made to the model for all phases

of work. Although a model validation was conducted as each phase was completed,

this report focuses on the Phase III model, since it is regarded as the most up-to-

date and accurate. Furthermore, the Phase III model will form the basis for the NFR

regional model. Enhancements were coordinated and implemented through a

collaborative effort of the City and MPO staffs to facilitate the effort.

Figure 2: Mason Street Model Phasing

INTRODUCTION


Trip Generation• Review and adjust CSU trip generation results

Trip Distribution• Adjust trip table (K-factors) to reflect survey data

Mode Choice• Update non-motorized mode split model with distance-based approach• Develop and calibrate mode choice model with transit capability• Update transit mode split process for non-Fort Collins zones

Traffic/Transit Assignment• Develop transit assignment module

Phase III: Top-Down Model Enhancements

Networks• Develop peak and off-peak speeds

Trip Generation• Develop cross-classification model (household size and income)• Add new trip purposes based on 1998 Mobility Report Card Data• Estimate new production and attraction trip rate models (1998 MRC data)• Incorporate allocation models• Update special generators

Trip Distribution• Estimate and calibrate parameters for new trip purposes (1998 MRC data)• Add peak and off-peak specificity• Develop speed feedback loop for consistency

Performance Reporting• Enhance performance module with air quality and other measures

Application Users Guide/Training

Mode Choice• Calibrate mode choice model for new trip purposes

Traffic Assignment• Develop time-of-day assignment routines

Data Collection and Processing• Conduct speed survey to estimate congested and free-flow speeds

INTRODUCTION


Travel behavior represented in the Mason Street Transportation Corridor model was

determined from data available at the corridor, city, and regional levels through

recently conducted data collection efforts. At the regional level, the household survey

administered through the 1998 Mobility Report Card process by the MPO provided

household-based trip information. Three transportation surveys undertaken by the

City of Fort Collins yielded corridor and city level data essential to the model

development effort. Finally, traffic counts were available from several entities for

validation of the model.

1999 Fort Collins Transportation SurveysTo support efforts on the Mason Street Transportation Corridor and other planning

activities in the City, Fort Collins conducted three transportation surveys in the fall

of 1999. The City’s consultant administered vehicle intercept and onboard transit

surveys and a special generator study at the Colorado State University (CSU)

campuses. The surveys were designed to provide specific travel behavior information

for the Mason Street and College Avenue corridors as well as citywide. These surveys

and their applicability to the model’s development are described below.

Colorado State University Special Generator Study

The CSU campuses are strategically important to the Mason Street Transportation

Corridor because they are major trip attractors lying in the heart of the Corridor and

because of the high transit and non-motorized uses associated with travel to and

from the campuses. The study consisted of cordon counts and a random survey of

student, visitor, and employee travel behavior.

The CSU Special Generator Study focused on trips made to and from the main

campus and the Veterinary Teaching Hospital. Personnel were stationed around the

perimeter of the campuses on the survey days to count the number of people and

vehicles coming onto each campus. Surveyors randomly interviewed students,

visitors, and employees in high traffic areas throughout the campuses. In addition,

surveys were distributed to faculty in their mailboxes and through contacts in several

departments. The CSU main campus and the veterinary campus were surveyed on

separate Wednesdays during the last two weeks in September 1999.

Special generator studies such as the one conducted at CSU are used to gather

travel behavior information at establishments that are particularly difficult to survey

through other traditional methods. They are similar in nature to a workplace

establishment survey but are focused on a specific site. Information obtained from

a special generator study includes trip attraction rates for enhancing trip generation

models, trip length data by trip purpose for specifying trip distribution models,

mode of arrival, and origin and destination patterns to and from the site. In effect,

the special generator study supplies valuable information at all levels of the four-

step modeling process.

The CSU campuses in Fort Collins are well suited for a study of this type. The

campuses are part of the Mason Street/College Avenue corridors and generate a

significant amount of travel activity that impacts these corridors. Other surveys for

the Mason Street Transportation Corridor project cannot capture sufficient information

at CSU to make significant inferences into the detailed travel behavior of the student

and worker populations there. The separate campus locations can be well isolated

in order to obtain specific information.

Data Collection and Analysis

DATA COLLECTION AND ANALYSIS


University students tend to have a high propensity for transit usage. Data from this

survey was used to plan short and long-term transit opportunities in the Mason

Street and College Avenue corridors and on radial routes serving the area.

Detailed information on the study’s conduct, data expansion, and the resulting

travel behavior data used in the model’s development is contained in Fort Collins

Transportation Surveys—Technical Report No. 3, 1999 Colorado State University

Special Generator Study (March 2000).

Mason Street Transportation Corridor Vehicle Intercept Survey

The Vehicle Intercept Survey was conducted on the streets in and around the College

Avenue and Mason Street corridors. Origin-destination, trip purpose, and auto

occupancy data were collected from motorists, bicyclists, and pedestrians by means

of a handout-mailback method. Survey sites were located at key intersections or

mid-block between two cross streets. Traffic control devices (e.g., signs and cones)

were used to alert drivers to the survey and slow traffic. The surveys were conducted

in the outbound direction away from the Mason Street Transportation Corridor.

The Vehicle Intercept Survey was conducted between Tuesdays and Thursdays during

the last two weeks of September 1999. Surveys were conducted at 19 locations

around the corridor over the six survey days. At each survey site travelers were

handed a postcard containing eight questions regarding their current trip. The

travelers’ names and license plates were not recorded in order to maintain personal

privacy. Those receiving a survey postcard were asked to complete the form and

drop it in any mailbox. Postage was prepaid. Safety of surveyors and travelers was

a primary concern during this survey, so uniformed officers from the Fort Collins

Police Department or the Larimer County Sheriff’s Department were stationed at

each survey site.

Detailed information on this survey is contained in Fort Collins Transportation

Surveys—Technical Report No. 1, 1999 Mason Street Corridor Vehicle Intercept Survey

(March 2000).

Transfort Onboard Transit Survey

The Transfort Onboard Transit Survey was conducted during the last two Wednesdays

in September 1999. Virtually all daytime and most evening routes were surveyed.

Origin-destination, trip purpose, household size, and other data were collected from

patrons of Transfort, the transit provider for the City of Fort Collins. Surveyors

handed a short survey questionnaire to each boarding Transfort bus patron during

the onboard survey. Passengers were asked to fill out the surveys and return them

at the end of their bus trip. The back of the survey form was printed with a postage-

paid return mail address to give passengers the option of filling out the form and

dropping it in the mail at a later date. Surveyors also counted the number of riders

boarding each bus.

The information gained from the onboard transit survey provided reliable data

about the travel behavior of transit users in Fort Collins. Arguably, this behavior

data is transferable to other cities throughout the North Front Range. The information

was translated into the travel model to provide for a more accurate estimate and

prediction of travel for the Mason Street Transportation Corridor subarea. Detailed

survey information can be found in Fort Collins Transportation Surveys—Technical

Report No. 2, 1999 Transfort Onboard Transit Survey (March 2000).

1998 Mobility Report Card Household SurveyIn 1995, the region’s MPO, the North Front Range Transportation & Air Quality

Planning Council, undertook a major project to track changes in key regional travel

characteristics including traffic congestion and use of travel demand management



measures. As part of this Mobility Report Card (MRC) effort, they initiated a household

travel survey that would occur approximately every two years. The most recent

household survey took place in 1998 and provided valuable information on

household-based trip-making that was used in several places in the travel model

during its development.

Almost 1,100 households making almost 11,000 trips were surveyed in the MRC

effort. Households and trips were weighted by jurisdiction but not expanded in the

initial effort. Among other variables, household size and income were collected.

Since cross-classified trip generation rates were desired for the model update,

expansion of the data was necessary to represent the regional number of households

and trips. In some cases, household size or income were not reported, which reduced

the number of usable households to 923 and their associated trip-making to 9,163

trips. These figures are unweighted and unexpanded, so the number of trips per

household calculated from these values is not representative of the region.

In order to expand the data to represent the region’s households and travel behavior,

a data expansion process that involved the use of the MPO’s socioeconomic data

and the 1990 Public Use Microdata Sample (PUMS) database was utilized. The MRC

data reported income in fifteen categories, which were aggregated into the three

income groups used in the model. Since the regional socioeconomic data contains

income group definitions but not household size, the 1990 PUMS data was utilized

to estimate the distribution of households by size and income groups. The PUMS

income data was correlated with the regional socioeconomic data and adjusted for

consumer price indices between 1990 and 1998 for the Denver area to produce the

distribution. The distribution was then adjusted to match the 1998 NFR household

control totals and divided into the regional distribution to determine the expansion

factors. Smoothing of the expansion factors over some strata was necessary due to

the low number of households that occurred in some strata. The final expansion factors

for the 1998 MRC households and trips are shown in Table 1.

Table 1: 1998 Mobility Report Card Expansion Factors

1 2 3 4 5+Low 446 148 231 192 217Medium 128 148 231 192 217High 183 147 235 133 276

Income GroupHousehold Size

The resulting number of expanded households weighted by jurisdiction is contained

in Table 2.

Table 2: 1998 Mobility Report Card Weighted and Expanded RegionalHouseholds

1 2 3 4 5+Low 15,004 2,660 1,106 767 246 19,784Medium 19,164 30,402 15,424 12,240 7,289 84,520High 4,347 19,637 14,952 11,373 9,904 60,214Total 38,516 52,699 31,483 24,380 17,440 164,518


Total

The resulting number of expanded trips weighted by jurisdiction is contained in

Table 3.

Table 3: 1998 Mobility Report Card Weighted and Expanded Trips

1 2 3 4 5+Low 60,044 19,794 13,915 4,297 4,680 102,728 Medium 103,396 305,448 210,566 222,262 171,724 1,013,396 High 25,977 215,549 211,329 235,813 265,326 953,994 Total 189,416 540,790 435,809 462,372 441,730 2,070,118


Total

Once expanded, the 1998 MRC data was applied in many places in the four-step

modeling process. Trip production and attraction rates were developed for each trip



purpose from the data; auto occupancies were calculated by trip purpose; time-of-

day information was produced; and so forth. Unfortunately, the address information

recorded for each trip end was not available, so geocoding of the addresses was not

possible and new average trip length calculations were not possible using the MRC

data.

Traffic CountsObserved traffic counts on roadways throughout the City of Fort Collins and the rest

of the regional modeling area are required for determining validation of the 1998

base year model. With assistance from the City and MPO staff, traffic counts were

collected from Loveland, Greeley, Fort Collins, and the Colorado Department of

Transportation (CDOT). Loveland provided a map that showed traffic counts and

the date the counts were taken. Greeley provided a GIS shape file with traffic count

locations and the year the counts were taken. Fort Collins has assembled an extensive

Access database with hourly counts taken throughout the City. The hourly counts

were summed over a 24-hour period to represent the daily counts necessary for

validation. CDOT provided counts along I-25 and other state highways in the region.

The Denver Regional Council of Governments (DRCOG) provided counts for the

Longmont area. All counts were then entered into TransCAD in the 1998 highway

network (*.dbd) file.

Once in the network file, the raw counts were adjusted to represent traffic in the

1998 base year. It is common practice to utilize counts from other years due to

limited data availability for a single year. It is, however, important to consider the

applicability of previous and future year counts as real-world roadway network

changes may render some counts unusable for some applications. This is especially

true for higher growth areas. For the 1998 Mason Street model, counts between

1994 and 1999 were utilized. An adjustment factor of 2.0 percent per year

(compounded) was applied to counts that were not collected in 1998.

Another adjustment made to the traffic counts was made to account for truck activity.

Raw traffic counts are typically reported as axles divided by two. Since many trucks

have more than two axles, adjustments are necessary so that the number of vehicles

is not over-reported. Limited data collected along College Avenue provided axle

factors for that facility. Since truck activity varies by area type and roadway functional

classification, additional information was necessary but not locally available. The

North Central Texas Council of Governments (NCTCOG), the metropolitan planning

organization for the Dallas-Fort Worth region, provided detailed truck factors used

in that area. By combining the two data sources with emphasis on the local data,

the truck factors shown in Table 4 were applied by dividing the raw counts by the

factors.

Table 4: Truck Factors for Adjustment of Raw Traffic Counts

Area Type Freeways ExpresswaysMajor

ArterialsMinor

ArterialsCollectors

Urban 1.150 1.023 1.023 1.023 1.023Rural 1.030 1.023 1.023 1.023 1.023



Socioeconomic data is the input activity-based information that provides the

foundation for trip-making in the travel model. Data is recorded for retail and non-

retail employment and households by three income groups. To facilitate the

development of a cross-classified trip generation model, household data is necessary

by income group and household size in order to more accurately model trip-making

across these strata. Estimation of household sizes using 1990 Census data and a

Fratar-based process is discussed in the Trip Generation section. Establishing the

employment and household data for the 1998 base year and 2020 horizon year is

described in this section.

The socioeconomic data is contained in geographically defined areas called traffic

analysis zones (TAZ). The TAZs are attached to the roadway and transit networks

through centroid connectors that allow vehicles and transit patrons to access the

transportation system. TAZs are sized and shaped to provide a relatively homogenous

amount and type of activity. In order to more accurately model detailed travel

movements in the City and region, the TAZ structure has been modified. This section

describes the changes made to the TAZs to achieve that objective.

Development of 1998 and 2020 Socioeconomic DataSeveral sources of regional socioeconomic information were available for various

years of analysis. So that the modeling effort for the Mason Street Transportation

Corridor project remains consistent with the regional modeling process, it was decided

that the MPO’s official 1995 and 2020 datasets should serve as the starting point

for development of the Mason Street model socioeconomic data. Simple interpolation

was used to arrive at 1998 socioeconomic data. Although a separate 1998

socioeconomic dataset had been developed as part of the 1998 MRC effort, it was

not used because it was not consistent with the official 1995 and 2020 datasets and

did not appear to have sufficient review and coordination with individual

jurisdictions. The interim Phase I and Phase II models used the 1998 MRC-based

dataset, but the final Phase III model used the interpolated 1998 information.

In some TAZs, the 1995 and 2020 data revealed negative trends with regard to

household and/or employment growth. In addition, the City’s Transportation Planning

and Advance Planning Departments reviewed and updated information in several

of the TAZs, particularly those in the northeast part of the City. Other suspect

information outside of Fort Collins was updated selectively as noted in the following

sections. The 1998 and 2020 data are contained in the Appendix with notations for

TAZs with information that has been adjusted manually.

1998 Socioeconomic Data

As noted previously, coordination with the City of Fort Collins’ planning staff and

the North Front Range MPO staff produced a methodology in which 1998

socioeconomic data was interpolated between the MPO’s official 1995 and 2020

datasets. The TAZs in the Mountain Vista area are the only exception to this method

because the City’s Advance Planning Department had separated those zones after

the 1995 and 2020 socioeconomic datasets were created. The 1998 socioeconomic

data for the Mountain Vista area was estimated by the Advance Planning staff.

Socioeconomic Data/Traffic Analysis Zone Structure

SOCIOECONOMIC DATA/


2020 Socioeconomic Data

With very few exceptions, the 2020 socioeconomic data is from the regional model

calibration effort documented in the November 1996 report Summary of 1995

MINUTP Regional Travel Model Update by TransPlan Associates. There were a few

TAZs in which the number of employees decreased from 1995 to 2020. These zones

were adjusted so the number of employees remained constant over time. As noted

previously, the TAZs in the Mountain Vista area were adjusted to reflect the zone

boundary and data changes from the City of Fort Collins’ Advance Planning

department. In addition, several TAZs reflect an adjustment of 2% per year growth

in total households. Finally, there were two instances in Greeley in which the

socioeconomic data in 2020 had been switched to an adjacent zone unintentionally.

These were not corrected since the employment and households in the combined

zones were reasonable.

All of the changes to the 1995 and 2020 “official” datasets were coordinated through

the City’s planning staff and the MPO staff so that the Mason Street Transportation

Corridor modeling process remained consistent with the regional planning process.

Changes to TAZ-level information appears in the socioeconomic data spreadsheet

in the Appendix. Many of these problems were not corrected because they would

not adversely affect the modeling of the Mason Street Transportation Corridor and

because they were outside the scope and jurisdiction of the Mason Street

Transportation Corridor project.

The final 1998 and 2020 socioeconomic data is summarized as follows for

households in Table 5 and employment in Table 6. Density plots showing 1998 and

2020 households and employment are shown in Figures 3-6.

Table 5: 1998 and 2020 Household Data

1998 2020 1998 2020 1998 2020 1998 2020Fort Collins 9,435 14,450 24,314 34,710 22,136 41,660 55,885 90,821Loveland 3,257 3,935 12,931 10,976 5,807 25,242 21,995 40,153Greeley 6,914 9,099 16,286 21,351 10,755 20,425 33,955 50,876Other/Rural 6,863 9,261 24,602 32,985 21,579 38,077 53,044 80,321Region 26,469 34,745 78,133 100,022 60,277 125,404 164,879 262,171

Total HouseholdsJurisdiction

Low Income Households

Medium Income Households

High Income Households

Table 6: 1998 and 2020 Employment Data

1998 2020 1998 2020 1998 2020Fort Collins 13,733 21,728 48,997 85,305 62,730 107,033Loveland 6,154 12,986 16,970 32,801 23,124 45,787Greeley 8,792 16,295 36,729 57,660 43,463 73,955Other/Rural 8,609 14,451 32,590 49,537 41,199 63,988 Region 37,288 65,460 135,286 225,303 170,516 290,763

JurisdictionRetail Employment Non-Retail Employment Total Employment

University of Northern Colorado

The University of Northern Colorado (UNC) in Greeley is located in traffic analysis

zone 285. The socioeconomic data for this zone showed 15, 37, and 200 total

employees, respectively, for the years 1995, 1998, and 2020. In the original MinUTP

model, UNC was treated as a special generator and, therefore, the number of

employees did not affect the model. With the implementation of additional trip

purposes including Home-based University trips, it was desirable to modify the

number of employees to correctly model the different trip purposes. UNC indicated

that they employed 2,138 full and part-time employees in the year 2000. This figure

was adjusted by 1.0 percent per year to estimate 1998 and 2020 employment.

TRAFFIC ANALYSIS ZONE STRUCTURE


Figure 3: 1998 Household Density Figure 4: 2020 Household Density

SOCIOECONOMIC DATA/


Figure 5: 1998 Employment Density Figure 6: 2020 Employment Density



Although UNC is clearly out of the Mason Street modeling domain, it was desired to

update the UNC figures so that the regional Home-based University trips would be

reasonable.

Adjustments to the Traffic Analysis Zone StructureAs mentioned previously, TAZs are sized and shaped to provide a relatively

homogenous amount and type of activity. With aggregate travel modeling of

households and employment as zonal summations, the desire for increased zonal

detail must be balanced with longer processing times that result from more zones.

In order to more accurately model detailed travel movements in the City and region,

the TAZ structure has been modified to some extent. This generally means that

TAZs were split into additional, smaller zones.

There were several areas where zone splits were made. The Mountain Vista area in

northeast Fort Collins was split into several new zones based on the recent Mountain

Vista Plan and City Plan adjustments suggested by the City. The CSU main campus

was split north/south into two zones: a western zone for the residence halls and a

eastern one containing the administrative offices and classrooms. Likewise, there

were a few other zones, primarily in the central and southern parts of the Corridor,

that were split to provide more accuracy for the Mason Street modeling effort.

Other TAZs in the City and region were split as well. For example, TAZ 11 in Berthoud

was split into several zones to reflect additional zonal detail in the 2020

socioeconomic dataset. The 1995 and 1998 data were split for TAZ 11 accordingly.

The following is a list of zone splits shown in Table 7 that were made to the TAZ

structure for the Mason Street Travel Demand Model.

Table 7: Adjustments to the TAZ Structure

Original TAZ

TAZs After Disaggregation

11 543, 544, 545, 546, 547, 548, 54940 40, 687, 68841 41, 630, 634, 689, 69043 43, 668, 66944 44, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 68245 45, 683, 684, 685, 68647 47, 69556 56, 69157 57, 640, 641, 64258 58, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658 59 59, 687, 660, 661, 662, 663, 664, 665, 666, 66761 61, 626, 627, 62862 62, 629, 631, 632, 633, 635, 63663 63, 637, 63873 73, 63975 75, 624, 62576 76, 622, 62384 84, 696

103 103, 697110 110, 698



Roadway and transit networks comprise basic input information for use in the

travel demand model and should represent real-world conditions to the extent

possible. Horizon year 2020 networks begin with the 1998 base year network and

include additional capacity from lane widenings, roadway networks, and new transit

services that are anticipated through transportation infrastructure investments

documented in plans and programs. Alternative transportation investments are

coded in the horizon year networks for analysis and comparison to the 2020 base

networks.

In the model, the networks are used to distribute trips, determine mode shares, and

route transit and automobile trips in the assignment modules. In addition, the

networks provide a foundation for system performance analysis including vehicle

miles of travel, air quality/emissions, level of service, and other performance

dimensions.

Roadway Networks1995 Roadway Network

The 1995 roadway network was imported from MinUTP to TransCAD using standard

TransCAD utilities. The City of Fort Collins’ conflated the line network for the region

based on the City of Fort Collins “onelinestreet” file and the Tiger files from Caliper

Corporation. Conflation refers to the process of aligning the roadway segments in the

network to actual, physical locations using the geographic information system (GIS)

capabilities of TransCAD. It was determined that the lengths calculated with TransCAD’s

GIS capabilities were more accurate than the distances coded in the original MinUTP

network. Therefore, the conflated distances are used in the TransCAD model.

In the MinUTP model, there were two links for each roadway segment, an A-B link

and a B-A link, to simulate opposite directions of two-way streets. Freeway and

one-way arterial streets were coded with only one link direction. When the network

was converted to TransCAD, the two links were converted for each roadway segment.

All of the original two-way link directions were combined into one link with two

directions each. This aided in displaying information as well as data entry. The

network was critically examined with the use of color and scaled symbol themes to

verify and correct any mislabeled links. When errors were found, the network was

updated to reflect the most accurate, real-world condition possible. Facility types,

area types, speeds, capacities, number of lanes, and other network attributes were

checked for consistency and reasonability within the entire North Front Range region

but with a focus on the Fort Collins and Mason Street Transportation Corridor areas.

1998 Roadway Network

The 1998 roadway network was developed by starting with the converted and

conflated 1995 network. Conversion and conflation of the 1998 network was

considered too resource consuming. Comparing the 1995 and 1998 MinUTP networks

yielded no roadway changes between the two years, although roadway investments

proceeded in the physical world from 1995 to 1998. There were three updates to the

1998 roadway network made in the City of Fort Collins: Timberline was extended to

the north, JFK Parkway was continued to Harmony, and Shields was widened from

2 to 4 lanes from Horsetooth to Harmony. Consistency checks were made continuously

throughout this process to ensure the highest quality network possible.

Networks

NETWORKS


Figure 7: 1998 Roadway Network

Congested and free-flow speeds and time-of-day capacities were added to the 1998

roadway network as the model enhancement occurred. The speeds were based on a

speed survey conducted in late Spring 2000. Table 8 shows the free-flow speeds

determined from the survey effort and added to the model. They vary by facility

type and area type and are used to distribute midday and off-peak period trips in

the trip distribution module. With the new speed feedback loop added to the model,

the free-flow speeds are replaced with link-specific off-peak speeds from the off-

peak period traffic assignment and are used in the trip distribution and mode choice

models. However, since the original input free-flow speeds are necessary for use as

a starting point for the equilibrium traffic assignment process and the speed processor,

they are retained throughout the modeling process.

Table 8: Input Free-flow Speeds (mph)

Functional Type Urban Rural CBDFreeway 78 78 n/aExpressway 40 65 n/aMajor Arterial 34 57 26Minor Arterial 30 48 17Collector 25 35 15Zone Connector 16 25 16Freeway Ramp 30 30 n/a

Table 9 shows the congested speeds determined from the survey effort and added to

the model. They vary by facility type and area type and are used to distribute morning

and evening peak period trips in the trip distribution module. With the new speed

feedback loop added to the model, the congested speeds have been replaced with

link-specific congested speeds from the AM-peak period traffic assignment and

iterated to closure. As a result, the input congested speeds are overwritten and are

not carried on the networks. They are reported here for reference and will be discussed

in detail in the traffic assignment, speed feedback, and performance chapters.

Table 9: Input Congested Speeds (mph)

Functional Type Urban Rural CBDFreeway 75 75 n/aExpressway 35 65 n/aMajor Arterial 32 54 18Minor Arterial 27 47 15Collector 25 35 15Zone Connector 16 25 16Freeway Ramp 30 30 n/a

Capacities coded on the network were not changed from the original MinUTP model.

NETWORKS


They are shown in Table 10. Time-of-day capacities are discussed in a later chapter.

Table 10: Level-of-Service C Roadway Capacities(vehicles per hour per lane)

Functional Type Urban Rural CBDFreeway 1500 1750 1500Expressway 1000 1200 1000Major Arterial 800 800 700Minor Arterial 550 550 435Collector 400 400 435Freeway Ramp 800 800 800

2020 Roadway Network

The 2020 roadway network was created by updating the 1998 roadway network

and adding capacity from lane widenings and new roadways to reflect 22 years of

transportation investment. The 1998 and 2020 MinUTP networks were compared

and changes were made in the 2020 TransCAD roadway network. Within the City of

Fort Collins, the 2020 highway network was developed to be consistent with the

City’s Master Street Plan. As with the 1998 network, the input free-flow speeds are

retained for use in traffic assignment and the speed processor. Speed feedback is

implemented in the same manner as in the 1998 network and discussed in more

detail in a subsequent chapter.

Centerline miles and lane miles by area type and functional type for the North Front

Range modeling domain are shown in Tables 11 and 12 for 1998 and 2020.

Table 11: Roadway Network Centerline Miles

Urban Rural CBD Urban Rural CBDFreeway 13 133 0 18 128 0Expressway 23 49 0 33 55 0Major Arterial 122 141 6 175 124 8Minor Arterial 184 314 8 155 330 7Collector 204 400 6 261 382 6Zone Connector 185 314 2 197 304 2Freeway Ramp 2 16 0 3 16 0Total 733 1367 22 842 1339 23

Functional Type1998 2020

Table 12: Roadway Network Lane Miles

Urban Rural CBD Urban Rural CBDFreeway 26 266 0 36 255 0Expressway 88 189 0 155 218 0Major Arterial 409 313 20 662 334 25Minor Arterial 505 632 25 377 664 19Collector 417 843 14 524 807 15Zone Connector 2592 4390 32 2758 4253 32Freeway Ramp 2 16 0 3 16 0Total 4039 6649 91 4515 6547 91

Functional Type1998 2020

Transit NetworksThe City of Fort Collins created the peak and off-peak 1998 and 2020 transit route

systems with the TransCAD software. Minor roadway network additions were needed

to accommodate the routes. Transit links in the roadway networks were added

where necessary to code the bus routes accurately. Transit-only links were created

on the Mason Street Transportation Corridor, where vehicles are not allowed to

travel because a dedicated busway operates on the link.

NETWORKS


Figure 8: 1998 Transit Route Structure on Roadway Network

Transit Line Coding

City of Fort Collins staff performed transit line coding using TransCAD coding

techniques. They performed literal coding of the networks including actual locations

of bus stops and developed a relationship for transit speeds based on highway

speeds to match scheduled transit travel times.

Literal coding of transit stops can result in as many as four separate stops for one

intersection (e.g., one for each intersection approach). This amount of coding detail

is beyond the amount necessary for transportation planning. With the MinUTP

modeling software used previously, such detail would greatly increase modeling

effort and, likely, modeling errors. Specifically, it would be necessary to code walk

transfer links between each of the stop nodes to provide for transfers between transit

lines.

However, TransCAD automatically aggregates stop nodes within a specified distance

up to 0.25 miles of a specified stop node when processing the transit networks. To

prevent unwanted combination of stop nodes (e.g., in the downtown area), the

threshold value has been set substantially below this value. Assuming eight blocks

per mile implies that the threshold value should be less than 0.125 miles. Based on

a review of path-building results, transit stops were merged within 0.05 miles and

non-transit nodes were merged to transit stops within 0.17 miles. In future

applications, the resulting aggregate network should be checked for reasonability.

If stop nodes are combined when, in fact, they should not be, TransCAD procedures

to exclude certain stop nodes from the combination process should be implemented.

As development and implementation of the transit networks occurred, a nuance in

the manner in which TransCAD merged stop nodes surfaced that caused significant

concern. TransCAD only allows one stop node to be merged to a route node. For

routes that traversed loops, this did not appear to be a problem. However, for those

routes that traveled back and forth on the same street, access and egress to the

transit line could only occur to and from one direction, or stop node. Transit patrons

desiring to access or egress the system from the other direction are forced to travel

to the end of the line and loop back, thus adding a significant amount of unnecessary

circuity to the path. In the current version of TransCAD, it appears the solution to

this problem is to code the transit routes operating back and forth on a single street

NETWORKS


as two one-way routes. Due to resource constraints, new transit networks were not

created. If the transit networks are re-created in the future, a recalibration of the

mode choice constants would be in order.

The transit operating speed has been coded as the roadway speed minus 10 miles

per hour. The 10 miles per hour decrease accounts for travel time losses due to

dwell time at bus stops combined with lower acceleration and deceleration rates

than automobiles. Table 13 compares the resulting modeled travel times for lines

with their scheduled travel times for selected routes. In general, there is a good

match between the scheduled and modeled times.

Table 13: Modeled and Scheduled Bus Travel Times

Route Origin to DestinationTransfort

Scheduled Time Modeled Run Time

(minutes)1 North Transit Center to College

and Troutman24 18

1 Front Range Community College to North Transit Center

28 25

3 Colorado State University to Overland and Elizabeth

11 13

6 Dunbar and Birmingham to Colorado State University

19 16

6 Colorado State University to Dunbar and Horsetooth

20 19

9 EPIC to North Transit Center 18 1511 Colorado State University to Plum

and Constitution9 8

Park and Ride Lot Coding

Formal park and ride lots do not exist in the 1998 transportation network.

Nevertheless, formal park and ride lots were necessary for year 2020 Mason Street

Transportation Corridor transit alternatives. The coding of auto access to transit is

considered more fully in the walk and drive access coding section.

Relatively standard techniques for coding park and ride lots developed for other

studies have been used. These techniques have been developed to provide for easier

summary of walk and drive access to transit lines at transit stations and other park

and ride lots and to minimize path-building difficulties with some travel modeling

programs. Figure 9 shows a typical coding technique and the one used in the Mason

Street model application. The focus of the technique is to (1) separate walk access

from drive access and (2) to provide a technique to account for travel impedance at

park and ride locations (e.g., walk time from parking spots to the actual transit pick-

up points).

As can be seen in Figure 9, the park and ride lot coding convention separates walk

access from drive access (and, when limited stop or fixed guideway transit is considered,

from other transit access). To accomplish this, three new facility types were added:

drive access links, walk access links, and walk access from park and ride links.

Figure 9: Park and Ride Coding

P-N-R NODEROUTE STOP WALK LINK(zero travel time)

TRANSIT LINE

WAL

KACCESS LINKS

DRIVE ACCESS LINKS

NETWORKS


When park and ride coding is conducted, it is important to ensure that TransCAD

does not aggregate the special walk links into the node designating the park and

ride station. Also, it is necessary to ensure that the software does not “reconnect”

the walk access network directly to the park and ride station.

Walk and Drive Access Coding

Each zone with any area within two miles of a transit line was connected to transit

via walk connectors. The walk connectors are abstract, straight-line connectors

from zone centroids to transit stops (or park and ride walk access nodes). Straight

line distances have been coded to simulate walking distances from the center of the

zone to the transit stop node. These distances are used to determine transit paths.

The walk access and egress distances are “stripped out” of the transit travel times

and replaced with estimated times for short walk access (less than one-half mile

from transit), medium access (one-half to one mile from transit), and long access

(more than one mile from transit). To facilitate this process and improve the estimates

of transit mode shares, special zonal data are summarized. The data specify the

proportion of each zone within short walk (less than one-half mile) and medium

walk (one-half to one mile) of transit. Long walk is any proportion of the zone

outside of medium walk range, but within 1.5 miles of transit (30 minute walk

time). The walk distance has been determined using TransCAD’s GIS capabilities to

determine geographic coverage around stops or the geographic areas within the

specified distances around actual transit stops. The portions of zones within the

specified distances were compared to the total areas of the zones to determine the

proportions of the zones within the walk access distance ranges.

The maximum walk distance for each zone was also recorded. The maximum walk

is used to estimate the average walk distance to be applied for walk access. For

example, if 100 percent of the zone is within 0.5 miles, the short walk proportion

would be 1.0, the medium and long walk proportions would be 0.0, the maximum

walk distance would be 0.5 miles, and the average walk access distance used in

mode choice would be 0.25 miles.

Drive access to transit links have also been coded. The drive access has been coded

to formal park and ride locations (for future year networks). Specific network links

have been coded from zone centroids to the appropriate transit network locations.

Travel times and travel distances on the links reflect travel times and distances over

the actual roadway network. While it is tempting to simply include the roadway

network as the “underlying” auto access network, this procedure causes illogical

results since “shortest” transit paths avoid transit (since transit is, in general, slower

than auto travel). Within the area covered by transit, TAZs have been connected to

all formal park and ride locations within five miles of the zone centroid. For the

Mason Street Transportation Corridor application, informal park and ride lots were

not coded, but the capability remains.

NETWORKS


The trip generation module of the Mason Street Multi-modal Travel Demand Model

was extensively redeveloped as part of the Phase III enhancements. Additional trip

purposes were added to provide increased sensitivity to specific travel markets. A

cross-classified trip generation model was developed with household income and

household size strata used to define the trip production models. To support the

cross-classification, a Fratar-based process was incorporated to estimate households

by size and income group for the base and horizon years. Expanded household

survey data from the 1998 Mobility Report Card effort and the 1999 CSU Special

Generator Study provided updated trip rates for the new trip purposes. Trip production

and attraction allocation submodels were applied as necessary. Finally, internal/

external and special generator trips were updated as part of this exercise.

Compared to the previous trip generation model, the enhanced model does not

include jurisdictional trip rate adjustments, and the trip rate adjustment factor of

0.9692 from the previous model has been removed. The following sections describe

these enhancements and trip generation results in detail.

Estimation of Zonal Household Size and IncomeDistributionsIn order to implement the cross-classified trip production models desired for the

Mason Street travel model, estimates of household income and household size

were necessary for all traffic analysis zones in the 1998 base year and 2020 horizon

year. Household incomes are defined in the MPO’s socioeconomic datasets as low,

medium, and high. Unfortunately, there does not appear to be consistency among

jurisdictions in the region as to the exact definition of these income groups.

Compounding the problem is the absence of any information regarding population

or household size in the socioeconomic datasets. Therefore, a Fratar-based process

was incorporated to estimate distributions of household size and income group.

The process began with data available on the TransCAD disks that report extracted

STF3A and STF3B data tabulations from the U.S. Census Bureau for 1990. This

information is formatted geographically at the census block level. By overlaying the

census blocks on the TAZ structure of the Mason Street model using TransCAD’s

integrated GIS functions, 1990 household size and income distributions were

estimated for each zone. Income definitions were determined by calculating the

percentage of households in each category of the 1998 socioeconomic dataset and

applying them to the income ranges used in the census data. The process resulted

in 1990 household size and income distributions for each zone in the region. These

distributions apply only to the marginals of the cross-classification strata, not the

necessary bivariate distribution for each zone.

It became obvious when completing the socioeconomic distribution Fratar process

that the majority of the zones had assigned all households to one income level. This

distribution made the Fratar process very difficult to calibrate. The census data was

available with income distributions by block group. It was determined that using

the census data distributions yielded much better results with the Fratar process.

The census data distribution was used for all major areas that did not distribute

households by income group. Loveland had already distributed households by income

group, so the distribution was not changed to the census data distribution.

Trip Generation

TRIP GENERATION


To arrive at the bivariate distribution for each zone, a 3x5 seed matrix of income

and household size groups for the region is Fratared to match the marginal

distributions of household income and size marginals for each zone. The Fratar

takes place over all TAZs to match regional distributions of households by income

group and household size. This procedure effectively replaces the household income

distribution for those zones containing households in only one income group with

the 1990 census-derived distribution. This was done to more accurately simulate

real-world socioeconomic conditions at the zonal level. The Fratar process produces

the bivariate distributions for each zone that can be applied to trip production rates

in the cross-classified trip production models described later in this chapter.

The resulting regional bivariate distributions by household size and income group

for 1998 and 2020 are shown in Tables 14 and 15. The base 1998 and 2020

households by income group from the original socioeconomic datasets are shown

for comparison.

Table 14: 1998 Households by Size and Income Groups

IncomeOne-person Households

Two-person Households

Three-person Households

Four-person Households

Five or more person

HouseholdsTotal

1998 Socio-economic

Data

Low 14,434 5,881 3,236 1,931 987 26,469 26,475Medium 19,229 27,142 13,603 10,956 7,203 78,133 78,137High 4,810 19,824 14,785 11,553 9,305 60,277 60,272Total 38,474 52,847 31,624 24,440 17,494 164,879 164,884

Table 15: 2020 Households by Size and Income Groups

IncomeOne-person Households

Two-person Households

Three-person Households

Four-person Households

Five or more person

HouseholdsTotal

2020 Socio-economic

Data

Low 20,842 8,029 4,135 2,450 1,288 36,745 36,745 Medium 27,682 34,192 16,509 13,158 8,482 100,022 100,022 High 12,653 41,810 29,640 23,254 18,047 125,404 125,404 Total 61,177 84,031 50,285 38,862 27,817 262,171 262,171

New Trip PurposesThe original MinUTP model contained three trip types: home-based work (HBW),

home-based nonwork (HBNW), and non-home-based (NHB). Additional trip purposes

were desired to (1) bring the travel model up to acceptable state-of-the-practice

standards for a medium-sized region such as the North Front Range, and (2) more

accurately model travel markets specific to the Mason Street Transportation Corridor.

College and shopping trips are significant trip purposes in the Mason Street

Transportation Corridor and important as potential transit trips. New trip purposes

have been added accordingly by splitting the HBNW trips into home-based university

(HBUNIV), home-based shop (HBSHOP), and home-based other (HBO) trips. In

addition, the NHB trips have been subdivided into two categories: work-based other

(WBO) and other-based other (OBO). HBW trips have not been subdivided, but new

trip rates have been estimated for this and all other purposes.

The sources of trip rates for the six purposes in the Phase III model were the 1998

MRC household survey and the CSU Special Generator Study. The household survey

collected information regarding trip purpose through the identification of activities

and the type of place visited for each trip. The place-based MRC database was

adjusted to reflect a trip-based definition so that an origin and a destination were

identified for each trip. Trip purposes were determined accordingly. For example, all

trips with one end at home and the other end at work were labeled HBW trips. The

remaining trip records were then filtered sequentially for HBUNIV, HBSHOP, HBO,

WBO, and OBO. In this manner, the trip types easiest to recognize and define were

identified first.

TRIP GENERATION


Trip Production Estimation and Allocation ModelsTrip production rates are household-based and derived from the trips reported by

household in the 1998 MRC household survey. Since the cross-classified production

models are household based and the survey is as well, there was no need to expand

the trips or households to arrive at the production rates. However, jurisdictional

weighting was necessary so that households are represented accurately in proportion

to the regional average. Fortunately, jurisdictional weighting factors were included

in the MRC data.

Trip purpose was defined for each trip in the MRC data as described previously.

Household size and income was contained in the data, but reported income ranges

did not correlate with the low, medium, and high income categories of the

socioeconomic data. Income definitions were determined by calculating the percentage

of households in each category of the 1998 socioeconomic dataset and applying

them to the income ranges used in the MRC data. Once that was done, the base trip

production rates by purpose and strata were calculated by dividing the number of

trips (by strata and purpose) by the number of households (by strata and purpose).

Several adjustments were made to the base trip production rates to arrive at the

final rates used in the model. First, home-based nonwork trip rates (HBSHOP, HBUNIV,

and HBO) were increased by ten percent to account for a perceived underreporting

of these trip types. Likewise, non-home-based trip rates (WBO and OBO) were

increased by fifteen percent. Furthermore, a portion of the HBO trips was reallocated

to the HBSHOP purpose through adjustment of trip production rates. This was done

to account for a perceived underreporting of HBSHOP trips. The adjustment was

done in accordance with the distribution of trips between HBSHOP and HBO purposes

from recent surveys conducted in Colorado Springs. Finally, trip production rates

were smoothed over combined strata in which the surveyed number of households

was lower than desired.

HBUNIV trips were significantly under-sampled in the 1998 MRC dataset. Therefore,

Colorado Springs trip rates were utilized for the Mason Street model’s HBUNIV

trips. Some smoothing and adjustment over income and household size strata for

application in the Mason Street Transportation Corridor model was necessary.

Final trip production rates used in the Mason Street model are shown in Table 16.

Table 16: Trip Production Rates

1 2 3 4 5+Low 0.25 0.51 1.49 1.49 1.49Medium 0.96 1.40 1.73 2.06 2.58High 0.63 1.70 2.27 2.27 2.27Low 0.27 1.20 1.20 1.20 1.20Medium 0.04 0.47 0.47 0.47 0.47High 0.04 0.07 0.47 0.47 0.47Low 0.27 1.20 1.20 1.20 1.20Medium 0.04 0.47 0.47 0.47 0.47High 0.04 0.07 0.47 0.47 0.47Low 0.70 0.70 0.70 0.70 0.70Medium 0.76 1.37 1.37 1.37 2.46High 1.17 1.37 1.37 1.37 2.46Low 1.33 2.56 3.14 3.14 3.14Medium 1.66 3.24 4.10 6.45 8.65High 1.96 3.24 4.10 7.98 11.13Low 0.30 0.52 1.58 1.58 1.58Medium 0.73 1.05 1.85 1.95 2.38High 1.06 2.05 2.41 2.47 2.70Low 0.87 1.74 0.87 0.87 0.87Medium 0.87 2.04 2.77 4.17 4.82High 1.11 1.80 2.25 4.38 4.82Low 3.73 7.23 8.98 8.98 8.98Medium 5.02 9.57 12.29 16.47 21.36High 5.96 10.23 12.87 18.94 23.85

PurposeIncome

Household Size

HBUNIV (CSU)

HBW

OBO

All

HBUNIV (UNC)

HBSHOP

HBO

WBO

TRIP GENERATION


A significant amount of relevant trip attraction information was collected in the

1999 CSU Special Generator Study. Thus, confidence in the HBUNIV attractions is

greater than the productions, so an approximate production model is suitable for

this application. As a result, HBUNIV productions are balanced against attractions

to the university campuses.

Production Allocation Model for Home-Based University Trips

The HBUNIV trip productions are estimated through application of the cross-classified

trip production rates multiplied by the number of households by size and income

for each TAZ. However, these household-based trips tend to be produced close to the

school campus and have very short trip lengths. As a result, a production allocation

model was incorporated. Since all of the HBUNIV attractions occur at either CSU or

UNC, distribution of these trips is effectively conducted within the trip generation

model through the allocation process.

The allocation process starts with the household-based production estimates, but

reallocates the trip productions using a right angle distance-based gamma function

that was calibrated from off-campus CSU student addresses. The distance-based

equation is applied to CSU and UNC separately. Correspondingly, productions and

attractions are balanced separately for CSU and UNC. Again, HBUNIV productions

are balanced against attractions, so the distribution of productions that results from

the allocation process is of much greater importance than the number of productions

estimated with the allocation model. The production allocation equation is:

])(198.9[ )(20.00.441 RADistif eRADistPP −− ∗∗∗=

where:Pf = final productions after allocationPi = initial estimate of productions

RADist = right angle distance from production zone to university

The resulting 1998 HBUNIV productions for the CSU and UNC campuses are shown

Figure 10: 1998 CSU Productions

TRIP GENERATION


in Figures 10 and 11 overlaid on the regional TAZ structure.

Work-Based Other Production Allocation Model

Work-based other (WBO) productions are estimated using the stratified household-

based trip production rates shown in Table 16. However, the nature of these trips is

such that most are produced at work locations and very few are actually produced

at home. Therefore, a production allocation model has been estimated to reorient

these trips more appropriately. Allocation factors are calculated by dividing the number

of weighted, expanded trips (by purpose and production land use) by the appropriate

socioeconomic variable (i.e., households, retail employment, non-retail employment).

The resulting WBO production allocation factors are shown in Table 17.

Table 17: Work-Based Other Production Allocation Model

Socioeconomic Allocation FactorHouseholds 0.14

Retail Employees 1.04Non-Retail Employees 1.49

Trip Attraction Estimation and Allocation ModelsTrip attraction rates were estimated using the weighted and expanded 1998 MRC

household survey data. The place and/or land use activity at the attraction end of

each trip was recorded in the survey and used to develop the attraction rates which

were calculated by dividing the weighted, expanded trips (by purpose and attraction

land use) by the appropriate socioeconomic variable (i.e., households, retail

employment, non-retail employment). Adjustments for underreporting and shifting

of HBO trips to the HBSHOP category were made consistent with the adjustment of

trip production rates.

Figure 11: 1998 UNC Productions

TRIP GENERATION


HBUNIV attraction trip rates are based on the cordon counts collected as part of the

1999 CSU Special Generator Study. They were calculated by dividing the number of

HBUNIV trip attractions by the number of full and part-time employees at CSU. The

same HBUNIV attraction rates apply to both CSU and UNC. The number of employees

at UNC in the 1998 and 2020 socioeconomic datasets were updated to more accurately

model trip-making for the campus. Attractions for other trip purposes to the CSU

and UNC campuses are treated as special generators due to the unique nature of the

trip-making at the university campuses. Special generators are discussed in the

following section.

Trip attraction rates for the Mason Street Multi-modal Travel Demand Model are

shown in Table 18.

Table 18: Trip Attraction Rates

Socioeconomic Variable HBW HBUNIV HBSHOP HBO WBO OBO

Households 0.05 0.00 0.00 1.20 0.23 0.46

Retail Employees 1.49 10.37 1.92 7.29 3.20 5.78Non-Retail Employees 1.49 10.37 0.00 2.75 0.77 0.81

Special GeneratorsThe trip generation model works well for those traffic analysis zones that are made

up of typical household and employment activity. However, there are some TAZs

that generate a greater amount of trips in comparison to the model’s estimation. In

other words, the trip rates at these locations are significantly greater than the base

trip rates used in the trip generation model. For these locations, special generator

values are applied in the model that define the number of trips produced and attracted

to the locations. The CSU main campus and veterinary teaching campus are two

examples of potential special generators.

As it turns out, the CSU veterinary hospital campus does not need special generator

treatment in the model. This is because (1) the vet hospital is relatively small in

comparison to the other university campuses, (2) it tends to function more like a

business (which it is), and (3) the campus is wholly contained within a zone with a

significant amount of other household and employment activity. On the other hand,

the CSU main campus trip rates are much greater than the base trip rates in the trip

generation model. Therefore, the main campus deserves special generator status in

the model and requires estimates of trip-making, which is available from data

collected in the 1999 CSU Special Generator Study.

The University of Northern Colorado (UNC) in Greeley is also treated as a special

generator in the model. Values for UNC are based on those from the original MinUTP

model, which shows no growth between 1998 and 2020. Likewise, Aims Community

College in Greeley is a special generator primarily because it was included as such

in the MinUTP model. The MinUTP model special generator values were used for

Aims, but these also show no growth between 1998 and 2020.

The special generator values are shown in Tables 19 and 20 for 1998 and 2020. The

CSU figures were calculated from the data collected as part of the 1999 CSU Special

Generator Study. Other figures are from the original MinUTP model. Since the special

generator function in the model replaces the number of trips generated by the trip

generation model, null and zero values are used for specific purposes. Zero values

TRIP GENERATION


replace the model generated values with zero. Null values indicate that the number

of trips calculated by the trip generation model are not modified.

Table 19: 1998 Special Generator Values in the Mason Street Model

CSU Class/Office

(TAZ 84)

CSU Dormitory (TAZ 696)

UNC (TAZ 285)

Aims (TAZ 271)

HBW-P 0 null null 500HBW-A 8,258 0 3,150 840HBUNIV-P 0 0 0 0HBUNIV-A null 0 null 0HBSHOP-P 0 null null 0HBSHOP-A 0 0 0 0HBO-P 0 null null 1,500HBO-A 0 0 6,750 1,800WBO-P 0 0 0 0WBO-A 0 0 0 0OBO-P 5,537 0 6,300 1,680OBO-A 5,537 0 6,300 1,680

Table 20: 2020 Special Generator Values in the Mason Street Model

CSU Class/Office

(TAZ 84)

CSU Dormitory (TAZ 696)

UNC (TAZ 285)

Aims (TAZ 271)

HBW-P 0 null null 500HBW-A 10,279 0 3,150 840HBUNIV-P 0 0 0 0HBUNIV-A null 0 null 0HBSHOP-P 0 null null 0HBSHOP-A 0 0 0 0HBO-P 0 null null 1,500HBO-A 0 0 6,750 1,800WBO-P 0 0 0 0WBO-A 0 0 0 0OBO-P 6,892 0 6,300 1,680OBO-A 6,892 0 6,300 1,680

Internal/External TripsInternal/external (I/E) trips are those trips that cross the regional boundary with

only an origin or a destination inside the region. They are modeled as productions

at the twelve external stations along the regional boundary at locations where

highways cross into the region. I/E trips are handled as an explicit purpose in the

model and have relatively long trip lengths. By their nature, they and the external/

external trips sum to equal the observed traffic count at the external station in the

base year. Since external/external trips require an external station survey for their

estimation and representation in the model, they have not been adjusted as part of

this model enhancement program.

On the other hand, I/E trips have been adjusted based on recent traffic data from

CDOT and other sources. For 1998, this adjustment was made by subtracting the

external/external trips from the observed traffic counts. These were compared to the

I/E trips that result from interpolation of the 1995 and 2020 I/E trip data and to the

TRIP GENERATION


I/E trips in the 1998 MRC dataset. For the year 2020, I/E trips have been reviewed

and in some cases adjusted from previous 2020 estimates. Per CDOT recommendation,

a guide of 5-7 percent per year for external stations at I-25 and 3 percent per year

for all other locations was subjectively applied. These growth rates were suggested

by CDOT based on historical traffic count data.

Internal/external trips have traditionally been input as low income households into

the MinUTP model, which has been a source of confusion and possible error. This

may have been done to minimize disk storage space, but is not considered a proper

technique. Therefore, the I/E trips were removed from the low income households

field and placed in a separate field (“IE Trips”) of the applicable socioeconomic

dataset.

While jurisdictional trip rate adjustments have been removed from the model for all

other trip purposes, they are retained for I/E trips based on the original MinUTP

model. They are shown in Table 21.

Table 21: Jurisdictional Trip Rate Factors for Internal/External Trips

JurisdictionAdjustment

FactorFort Collins 0.50Loveland 0.50Greeley 0.42Longmont 2.96Rural 1.27

Table 22 contains the internal/external trips estimated for 1998 and 2020.

Table 22: Internal/External Trips in the Mason Street Travel Model

External Station TAZ

Street Location

1998 Interpolated

1998 MRC Dataset

1998 CDOT Traffic Count and E/E Trip

Analysis1998 Mason

Model2020 Mason

Model299 US287 (S) 11,944 16,688 11,250 11,250 24,000 300 I-25 (S) 53,096 59,915 59,915 59,915 99,999 301 US85 (S) 18,960 33,379 18,050 18,050 37,000 302 SH119 (S) 29,576 38,629 29,750 29,750 47,000 303 SH263 (E) 3,576 3,169 3,169 3,169 8,900 311 US287 (N) 2,732 2,267 2,267 2,267 5,900 312 I-25 (N) 6,260 5,910 5,910 5,910 21,000 313 US85 (N) 1,728 1,770 1,770 1,770 2,300 314 SH14 (E) 1,936 1,810 1,810 1,810 2,200 315 US34 (E) 6,496 7,902 5,750 5,750 11,600 316 US34 (W) 9,344 6,930 6,930 6,930 17,000 317 SH66 (W) 12,116 8,411 12,600 12,600 25,500 Total 157,764 186,780 159,171 159,171 302,399

Balancing Productions and AttractionsTrip productions and attractions are estimated separately by purpose through the

models previously described. While an attempt is made to make the initial estimate

of productions equal to the initial estimate of attractions, it is not feasible to make

them exactly equal, which is necessary to ensure conservation of trips in the model.

The balancing process ensures productions are equal to attractions calculated by

the model, so it is conducted after the special generator values are applied. Balancing

depends on the level of confidence associated with the estimation of productions

and attractions.

Home-based trips are typically balanced to the production, or home, end of the trip

because that is where the trip information is often collected. Work trips can be

TRIP GENERATION


balanced to attractions if reliable attraction-end data is collected through a workplace

establishment survey, but that was not the case with the Mason Street model. One

exception to the home-based trips is HBUNIV, in which productions are balanced to

attractions due to the reliability of data collected in the cordon count of the 1999

CSU Special Generator Study. WBO trips are balanced to the initial estimate of

productions from the cross-classified production model. OBO attractions are balanced

to productions. Finally, internal/external (I/E) attractions are balanced to productions

at the external stations.

Productions and attractions estimated by the trip generation model after balancing

are shown by purpose for 1998 and 2020 in Tables 23 and 24.

Table 23: 1998 Productions and Attractions

Fort Collins Loveland Greeley Other/Rural TotalHBW-P 84,469 34,000 50,677 85,665 254,811 HBW-A 91,886 34,339 66,735 61,850 254,811 HBUNIV-P 62,526 2,307 21,325 607 86,766 HBUNIV-A 65,030 - 21,736 - 86,766 HBSHOP-P 70,958 28,499 42,758 71,030 213,246 HBSHOP-A 78,351 35,291 50,235 49,369 213,246 HBO-P 226,013 91,821 138,891 236,301 693,027 HBO-A 231,462 94,723 196,234 170,609 693,027 WBO-P 88,699 35,984 66,448 67,217 258,348 WBO-A 90,946 38,493 62,911 65,999 258,348 OBO-P 142,338 58,532 99,948 99,032 399,851 OBO-A 142,338 58,532 99,948 99,032 399,851 I/E-P - - - 159,171 159,171 I/E-A 30,013 12,052 16,885 100,221 159,171 Total Productions 675,004 251,144 420,049 719,022 2,065,219 Total Attractions 730,027 273,429 514,684 547,079 2,065,219

Table 24: 2020 Productions and Attractions

Fort Collins Loveland Greeley Other/Rural TotalHBW-P 139,128 66,332 76,990 130,721 413,171 HBW-A 152,291 65,470 102,786 92,624 413,171 HBUNIV-P 72,423 3,024 26,298 1,011 102,756 HBUNIV-A 75,711 - 27,045 - 102,756 HBSHOP-P 116,389 54,460 64,780 108,062 343,691 HBSHOP-A 113,755 68,346 85,535 76,055 343,691 HBO-P 372,802 180,949 210,851 359,572 1,124,174 HBO-A 393,365 180,462 295,038 255,309 1,124,174 WBO-P 154,706 69,472 105,656 102,248 432,082 WBO-A 149,493 75,818 104,220 102,551 432,082 OBO-P 217,166 110,349 154,349 147,560 629,424 OBO-A 217,166 110,349 154,349 147,560 629,424 I/E-P - - - 302,399 302,399 I/E-A 63,298 30,828 32,560 175,713 302,399 Total Productions 1,072,614 484,586 638,923 1,151,575 3,347,698 Total Attractions 1,165,079 531,274 801,532 849,813 3,347,698

The model’s estimated productions and attractions imply 1.49 and 1.42 HBW

attractions per employee in 1998 and 2020, respectively. Total trips per household

for these years is 12.53 and 12.67, which is slightly higher than other regions of

similar size but includes non-motorized bicycle and pedestrian trips. Productions

per household by trip purpose are shown in Table 25.

Table 25: Productions per Household

Trip Purpose 1998 2020HBW 1.55 1.58 HBUNIV 0.53 0.39 HBSHOP 1.29 1.31 HBO 4.20 4.29 WBO 1.57 1.65 OBO 2.43 2.40 Subtotal 11.56 11.62 I/E 0.97 1.14 Total 12.53 12.67

TRIP GENERATION


Pathbuilding is the process that determines the minimum path between all zone

interchanges and prepares output matrices of accumulated, or skimmed, values

along the paths. Paths on both the roadway and transit networks are built and

skimmed. The resulting values provide basic information for trip distribution and

mode choice.

Roadway PathbuildingThe original MinUTP model minimizes an impedance function based on time,

distance, and weighting factors to build shortest paths. The impedance function is:

IMP = [(TCOST * time) + (DCOST * distance)]/FCOST

where:

IMP = impedanceTCOST = weighting factor for time variable

time = estimated travel time between zonesDCOST = weighting factor for distance variabledistance = travel distance between zonesFCOST = weighting factor for impedance variable

The weighting factors used in the model are 40, 116, and 100 for TCOST, DCOST,

and FCOST, respectively. These values are from the original MinUTP model and

have not been changed. The FCOST parameter was used to ensure that individual

matrix cells (i.e., zone-to-zone impedance, distance, or time values) did not exceed

the memory limitations associated with the MinUTP matrix file format. This is no

longer an issue in the TransCAD environment, but the parameters are retained

nonetheless. The estimated travel time for the pathbuilding process varies depending

on trip purpose. HBW and HBUNIV trips are distributed using congested speeds

Pathbuildingand travel times due to their tendency to occur during peak periods. All other trip

purposes use the free-flow speeds and travel times for pathbuilding.

The values of impedance, distance, and time are skimmed (i.e., summed on the

shortest impedance path between each pair of zones in the region). The trip

distribution module uses the impedance function only. Time skims are used in the

mode choice model. Distance skims are used for reporting purposes.

The model calculates intrazonal impedances internally using the shortest zone-to-

zone path between each pair of zones multiplied by a factor of 0.50 to determine

that zone’s intrazonal impedance. This is known as the Nearest Neighbor Rule,

wherein the intrazonal impedance for a zone is based on the impedance to that

zone’s nearest neighbor. The intrazonal impedances are used by trip distribution to

determine intrazonal trips.

Terminal penalties have been applied in the model to the impedance skims, but not

to the distance and time skims. They simulate several travel-related variables such

as the time to locate a parking space, walking to a final destination, etc. Terminal

penalties are based on terminal times described in the November 1996 MinUTP

model documentation. Since they are applied directly to the impedance skims, they

actually represent impedance units, which are a function of both time and distance.

The terminal penalties are added to both ends of each zone-to-zone impedance

depending on the area type of the zones. Terminal penalties are shown in Table 26.

PATHBUILDING


Table 26: Terminal Penalties (impedance units)

Area TypeTerminal

ImpedanceUrban 2Rural 1CBD 4University 3External Station 5

Transit PathbuildingFor the Mason Street travel model, three sets of transit paths have been constructed:

• peak period walk access paths

• peak period drive access paths

• off-peak period walk access paths.

The specification of the three sets of paths implicitly assumes that drive access will

be used only for commuting and university trips that take place during the peak period.

Based on the 1999 Transfort Onboard Transit Survey, only seven drive access trips were

recorded for home-based work and about 40 for home-based university trips. Thus, the

above assumption cannot necessarily be verified or refuted by observed behavior. All

other transit travel (for all other purposes) is assumed to access transit via walk.

All transit paths are based on travel time only and have been constructed using

TransCAD’s “Pathfinder” path-building technique. The Fort Collins transit network

does not have sufficient fare options to warrant the construction of fare-weighted

transit paths. Transit fares on Transfort are, in general, very low and tend to be

highly subsidized. Children of age 17 and under travel free and each CSU student

receives a “free” annual transit pass that is subsidized through student fees charged

by the university. Although CSU students are, in fact, paying transit fares through

their fees, each ride has the appearance of being free since no cash fare needs to be

paid. Based on Transfort records, the average farebox recovery for each boarding

passenger (excluding subsidies from CSU student fees) is approximately 19 cents.

Based on the onboard transit survey, average fares per linked trip by trip purpose

have been estimated and applied in the model as shown in Table 27.

Table 27: Average Transit Fares by Purpose

Trip PurposeAverage

FareHBW $0.64 HBUNIV $0.00 HBSHOP $0.66 HBO $0.50 WBO $0.51 OBO $0.22

When the impacts of the numbers of transfers (which are free) are accounted for, the

overall average fare per unlinked trip (or boarding) from the survey is close to the

reported average fare of 19 cents per boarding.

The weights and parameters applied in the Mason Street Transportation Corridor

model’s transit path-building routines are shown in Table 28.

Table 28: Transit Pathbuilding Weights and ParametersComponent Weight

In-Vehicle Travel Time 1Transfer Time 1Wait Time 2.5Dwelling Time 1Non-Transit–Walk Access 4.68Non-Transit–Auto Access 4.68Inter-arrival Parameter 0.5Cost Threshold % 10Maximum Transfers 3

The following transit skims have been summarized for the transit paths:

PATHBUILDING


• fare

• number of transfers

• first wait time

• subsequent wait time

• in-vehicle travel time

• out-of-vehicle travel time

Transit paths between selected TAZs were built and checked for reasonability. These

checks focused on the reasonability of the path (i.e., would a reasonable traveler

choose the path estimated by the transit path-building routines?) and the

reasonability of the travel time components. After the selected transit paths were

confirmed as reasonable, the observed trip tables estimated from the transit onboard

survey data were assigned. Table 29 summarizes these results. The overall coefficient

of determination (r2) for the assigned to observed boardings by line is 0.98, which

shows a high degree of consistency.

Table 29: Assigned and Observed Boardings by Route

Observed Assigned1a and 1b 610 358 -252 -41%2, 2T, 3, 4, Evening 34, 11, and 11T 2628 2242 -386 -15%5 102 108 6 6%6 and 6T 303 495 192 63%7a, 7b and Evening 67 475 494 19 4%8 210 99 -111 -53%9 and 9T 364 173 -191 -52%10 184 142 -42 -23%14 0 11 11 n/aFoxtrot 188 0 -188 -100%Southside Shuttle 80 54 -26 -33%Nightlite 0 0 0 n/aAll Routes 5,144 4,176 -968 -19%

RoutesBoardings

DifferencePercent

Difference

PATHBUILDING


Trip distribution is the process through which productions and attractions from the

trip generation model are apportioned among all zone pairs by trip purpose. The

resulting trip table matrix contains both intrazonal (i.e., trips that don’t leave the

zone) on the diagonal and interzonal trips in all other zone interchange cells. The

TransCAD-based Mason Street model uses a standard gravity model equation and

applies friction factors to represent the effects of impedance between zones. As the

impedance between zones increases, the number of trips between them will decrease.

This is similar to the standard gravity model which assumes that the gravitational

attraction between two bodies decreases the further apart they are. The gravity

model also assumes that the gravitational attraction between the two bodies is

directly proportional to their masses. The trip distribution model makes a similar

assumption in that the number of trips between two zones is directly proportional

to their productions and attractions.

The gravity model equation used by trip distribution to estimate the number of trips

between each zone pair is as follows:

( )T P

A F K

A F Kij i

j ij ij

j ij ijj

n= ×× ×

× ×

�

�

��

�

�

��

=�

1

where:

Tij = trips between zone i and zone jPi = productions in zone iAj = attractions in zone jKij = K-factor adjustment between zones i and j

i = production zone

j =attraction zonen =total number of zonesFij =friction factor (a function of impedance between zones i and j)

K-factors are typically used in travel demand models to account for nuances in

travel behavior and/or the transportation system that cannot be accurately modeled

with simplified aggregate modeling techniques. They are often applied at the district

or jurisdictional level to adjust regional distribution patterns. In the Mason Street

travel model, K-factors were not necessary and have not been included. The capability

exists within the TransCAD model to incorporate K-factors. However, with version

3.61 of TransCAD, K-factors must be applied manually because the automated

scripting language does not recognize the K-factor adjustments. Therefore, it was

very desirable to develop the Mason Street model without K-factors.

When friction factors are calibrated for each trip purpose, there are implied average

trip lengths in terms of time and distance that result in the base year. Ideally, the

1998 Mobility Report Card, or MRC, data would be available so that recent trip

lengths in the region could be determined and incorporated in the model.

Unfortunately, the address information associated with the 1998 MRC household

survey was not available for processing during this effort. Had it been available, the

addresses could have been geocoded and the trip distribution patterns and trip

lengths could have been updated in the model. Furthermore, the reported trip lengths

recorded in the 1998 MRC database were not deemed reliable enough for processing

new trip lengths.

Since trip length data was not available, the base trip lengths from the original

MinUTP model were used with adjustments. The reported trip lengths from the

Trip Distribution

TRIP DISTRIBUTION


1998 MRC data were used to subdivide the original three trip purposes into the six

new purposes by means of a proportional distribution. A review of the documentation

from the previous updates of the MinUTP model did not indicate the source from

which the trip lengths were derived; however, it appears that the data was collected

previous to 1995 and possibly before 1990. Since trip lengths have typically increased

over time in growing regions, the average trip lengths from the original MinUTP

model were adjusted for application in the 1998 TransCAD-based Mason Street

model. The trip lengths were compared with those from other regions such as

Colorado Springs, and a ten percent increase was applied to all trip purposes except

HBW, HBUNIV, and I/E.

Tables 30 and 31 show the trip lengths used in the Mason Street model in terms of

distance and time.

Table 30: Average Trip Length (miles)

Trip PurposeOriginal 1995/1998 Minutp-

based Trip LengthsDesired 1998 Trip Lengths

1998 Mason Model

2020 Mason Model

HBW 8.5 8.5 8.6 8.8HBUNIV n/a n/a 3.2 3.6HBSHOP 4.5 5 4.9 5.7HBO 5.4 5.9 5.9 6.4WBO 4.7 5.2 5.2 5.5OBO 4.3 4.7 4.7 5I/E 26.8 26.8 26.5 27.8

Table 31: Average Trip Length (minutes)

Trip Purpose1998 Mason

Model2020 Mason

ModelHBW 17.1 22.0HBUNIV 9.4 10.7HBSHOP 9.8 11.9HBO 11.5 13.1WBO 10.7 12.4OBO 9.9 11.6I/E 34.5 45.8

In order to calibrate the trip distribution model, friction factors from the original

MinUTP model were converted to gamma functions and adjusted manually to arrive

at the desired 1998 trip lengths. The friction factors are a function of impedance

and are determined using the following equation:

FF = a*(imp)b*ec(imp)

where:

FF = friction factorimp = impedance

a, b, c = calibration parameters

The calibrated parameters by purpose are shown in Table 32. Friction factors for the

HBUNIV purpose are not necessary because distribution is performed on these trips

through the trip generation allocation submodel. Figure 12 shows the friction factor

equations for each purpose.

Table 32: Friction Factor Parameters

Trip Purpose Alpha (a) Beta (b) Gamma (c) HBW 6100 0.0000 -0.0828HBSHOP 7300 -0.4000 -0.2514HBO 5150 -0.1590 -0.1949WBO 3800 0.0400 -0.1300OBO 4000 0.0026 -0.1400I/E 2700 -0.0110 -0.0380

Figure 12: Friction Factor Curves

0

1,000

2,000

3,000

4,000

5,000

2 712 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97

102 107 112 117Impedance

HBW HBSHOP

HBO WBO

OBO I/E

TRIP DISTRIBUTION


OverviewAs shown in Figure 1, there are two components in determining the number of trip

interchanges by mode: non-motorized mode split and mode choice. The non-motor-

ized model is called a split model because it is based strictly on aggregate relation-

ships, not disaggregate, discrete choice theory. The model uses aggregate relation-

ships (i.e., average behavior) to determine the proportions of trips made by walk

and bicycle based upon trip distance.

In contrast, the mode choice model is based on disaggregate choice theory. The

choice model uses socioeconomic characteristics of travelers as well as travel times

and costs to determine the probabilities that travelers will choose one mode over

another. Both models have been transferred from other areas and updated to ensure

that they reproduce aggregate traveler behavior in the Fort Collins area. While there

are some fully integrated mode choice models that include non-motorized modes as

well as motorized modes in the choice set, there has not been as much experience

with the fully integrated models, so their application is questionable for the Mason

Street model.

Non-Motorized Mode SplitSince the 1998 MRC household survey data Could not be geocoded, an option for

estimating the non-motorized mode split models from that data did not exist. As a

result, the non-motorized mode split model has been transferred from the Colorado

Springs travel model. This model uses equations to estimate the proportions of trips

that are made by walk and bicycle based on the length of the trip as determined

from the roadway distance matrix. This model replaces the previous MinUTP model

procedure of factoring the person trip table by mode share constants. The revised

procedure results in varying non-motorized mode shares based on the spatial sepa-

ration of the TAZs being considered. This, by itself, is an improvement over previ-

ous model procedures.

One difficulty with transferring the Colorado Springs models was that the Colorado

Springs models disaggregated the home-based non-work trip purpose into compo-

nent parts: home-based elementary school trips, home-based high school trips,

home-based college-university trips, home-based shop trips, and home-based other

trips. Thus, it was necessary to reprocess the Colorado Springs data along with

known information about the Fort Collins area to develop Fort Collins specific mod-

els. To do this, the original Colorado Springs survey data was used to estimate the

non-motorized share by trip distance models. The models were then adjusted via an

iterative technique to match observed non-motorized shares for Fort Collins and the

region from the 1998 MRC survey.

The final non-motorized mode share models for all zones except the CSU zones (84

and 696) and the UNC zone (285) are shown in Table 33.

Mode Choice

MODE CHOICE


Table 33: Non-motorized Mode Share Models for All Zones ExceptUniversity Zones

Trip Purpose Mode Model Valid Range

Walk min{max[(0.852 – 0.680 x length0.5),0], [1.0 – (HBW bike shares)]}

0-1.571 miles

Bike max[(0.072 – 0.005 x length), 0] 0-13.534 miles

Walk min{max[(1.844 – 1.852 x length0.5),0], [1.0 – (HBUniv bike shares)]}

0-0.991 miles

Bike max[(0.168 – 0.143 x length0.5), 0] 0-1.387 miles

Walk min{max[(1.844 – 1.852 x length0.5),0], [1.0 – (HBShop bike shares)]}

0-0.991 miles


Walk min{max[(1.844 – 1.852 x length0.5),0], [1.0 – (HBO bike shares)]}

0-0.991 miles


Walk min{max[(0.947 – 0.880 x length0.5),0], [1.0 – (WBO bike shares)]}

0-1.159 mile


Walk min{max[(0.947 – 0.880 x length0.5),0], [1.0 – (OBO bike shares)]}

0-1.159 miles


WBO

OBO

HBW

HBUNIV

HBSHOP

HBO

The university zones attract many more bike and pedestrian trips than other zones.

Separate equations were used for these zones to adequately model the non-motor-

ized trips for these zones. The final non-motorized mode share models for the uni-

versity zones (84, 696, and 285) are shown in Table 34.

Table 34: Non-motorized Mode Share Models for University Zones

Trip Purpose Mode Model Valid Range

Walk min{max[(7.670 – 5.100 x length0.5),0], [1.0 – (HBW bike shares)]}

0-2.262 miles


Walk min{max[(7.670 – 5.100 x length0.5),0], [1.0 – (HBUniv bike shares)]}

0-2.262 miles


Walk min{max[(16.596 – 13.890 x length0.5),0], [1.0 – (HBShop bike shares)]}

0-1.428 miles


Walk min{max[(16.596 – 13.890 x length0.5),0], [1.0 – (HBO bike shares)]}

0-1.428 miles


Walk min{max[(8.523 – 6.597 x length0.5),0], [1.0 – (WBO bike shares)]}

0-1.669 mile


Walk min{max[(8.523 – 6.597 x length0.5),0], [1.0 – (OBO bike shares)]}

0-1.669 miles


WBO

OBO

HBW

HBUNIV

HBSHOP

HBO

The results of these models are shown in Table 35 in comparison to the observed

mode shares. The non-motorized equations are not based on jurisdictions, so the

models were calibrated based on the regional mode shares only.

MODE CHOICE


Table 35: Observed and Modeled Non-Motorized Mode Shares for 1998

Observed Modeled Observed Modeled Observed ModeledRegion 2% 3% 4% 4% 6% 6%Fort Collins 3% 4% 6% 4% 9% 7%Greeley 2% 3% 3% 7% 5% 10%Loveland 2% 1% 3% 2% 5% 3%CSU 20% 21% 13% 14% 33% 34%

AreaBicycle Mode Shares

Pedestrian Mode Shares

Non-Motorized Mode Shares

Motorized Mode ChoiceThe mode choice model for Fort Collins is applied for HBW, HBUNIV, HBSHOP, HBO,

WBO, and OBO trips made in motorized vehicles. The matrices for trips made in

motorized vehicles are estimated using the non-motorized mode split model dis-

cussed above.

For zones outside of Fort Collins, the vehicle trips are calculated based on a matrix

of transit shares and auto occupancy rates taken from the MinUTP model. Transit

mode shares in Loveland and Greeley are about one percent of all trips in those

jurisdictions.

The mode choice models are logit-based models transferred from other regions,

calibrated to reproduce observed mode shares for the City of Fort Collins. The cali-

bration focused on the Fort Collins area since an on-board travel survey had re-

cently been completed as part of the Mason Street Transportation Corridor study.

The results of that survey were expanded and used to determine the numbers of

HBW, HBUNIV, HBSHOP, HBO, WBO, and OBO trips made by transit. The estimated

transit trips were used in conjunction with the modeled motorized trips (resulting

from the non-motorized mode split model) to estimate mode shares by trip purpose.

Those mode shares formed the actual basis for the calibration of mode choice model

constants.

Two different model forms have been used for the mode choice model. The HBW

and HBUNIV mode choice models are nested-logit mode choice models and the other

mode choice models are multinomial logit models. Nested logit models represent the

current “best state-of-the-practice” for mode choice modeling and multinomial logit

models are still considered “good” modeling practice.

The standard logit formulation is:

�=

m

u

u

im

i

e

eP

where:Pi = the probability of using mode iUi = the utility of mode i

= a linear combination of impedances and, possibly,socioeconomic and locational variables

= ci + b1xi1 + b2xi2 + b3xi3 + …ci = constant for mode i

b1, b2, b3, … = estimated model coefficients for variables 1, 2, 3, …xi1, xi2, xi3, … = values for variables 1, 2, 3, … for model

Figure 13 shows the multinomial (actually, binomial) mode choice model used for

the Mason Street model’s HBSHOP, HBO, WBO, and OBO trips.

MODE CHOICE


Figure 13: Multinomial Logit Structure

Choice

Auto Transit

Nested logit mode choice models, such as the one specified for the Mason Street

model’s HBW and HBUNIV mode choice models offer an improvement over multi-

nomial logit models. Multinomial logit models assume equally competing alterna-

tives that allow “shifting” of trips to and from other modes in proportion to the

initial estimates of those modes. A common problem that results from this propor-

tional shifting is the violation of the “Independence of Irrelevant Alternatives” (IIA)

axiom.

For example, assume that there are two modes, auto and blue bus, each with a 50

percent mode share. Now assume that half of the blue buses are painted red and

that the red buses are treated as a third mode in the multinomial model. Since the

red bus would have the same utility as the auto and blue bus, each mode would

have a mode share of 33 percent. Obviously, the expected result is that the auto

would retain a 50 percent mode share and the remaining 50 percent would be split

between the red and blue bus services. In a nested logit model, the first level would

represent the choice between auto and bus service; and the second level below the

bus would represent the choice between red and blue bus service among the bus

riders. Since painting the buses would not change the composite utility of the bus

nest, the auto would retain its 50 percent mode share under this model structure.

Nested logit models recognize the potential for something other than equal (or

proportional) competition among modes. The structure for the HBW and HBUNIV

mode choice models (Figure 14) assumes that modes, sub-modes, and access modes

are distinctly different types of alternatives that present distinct choices to travelers.

Within each nest, the model operates on the modes included in the nest as a multi-

nomial logit model. Likewise, the model operates on nests included at a specific

nesting level as a multinomial logit model. However, the competition between modes

included in different nests or nesting levels is not in proportion to the initial esti-

mates of the mode shares. As a result, an important departure from multinomial

logit models is that “lower level” choices are more “elastic” than they would be in a

multinomial logit model.

The nested logit model employs several multinomial logit models. The first is the

choice among the primary modes: drive alone, shared ride, and transit. The second

model provides a choice between walk and drive access to transit. The third model

provides a choice among carpool sizes for shared ride: 2 person, 3 person, or 4 or

more persons. The fourth model provides a choice between walk access to local bus

or walk access to premium transit (express bus or rail). The final model provides for

a choice between drive access to formal or informal park and ride lots.

MODE CHOICE


Figure 14: : : : : Nested Logit Structure

Choice

DriveAlone

2Person

3Person

4+Person

Local Premium FormalLot

InformalLot

DriveAlone

SharedRide

Transit

SharedRide

WalkAccess

DriveAccess

DriveAlone

In application, utilities are estimated at the bottom levels first and passed up through

the nesting structure. When this is complete, the probabilities are estimated from

the top of the structure down. Composite utilities are passed upward through the

use of logsum variables. These composite utilities are the natural logarithms of the

denominators of the logit model. For example, for the composite walk access mode,

the logsum would be based on walk to local bus and walk to premium bus and

calculated as:

( )wpwluu

walk eeLOGSUM +−= ln

Likewise, the logsum of the composite drive access to transit mode would be calcu-

lated as:

( )didf uudrive eeLOGSUM +−= ln

The logsum terms for the walk access and drive access modes would then appear in

the multinomial choice model for transit access as:

drivewalk

walk

LOGSUMKLOGSUMK

LOGSUMK

walkee

eP

11

1

+=

and

walkdrive PP −=1where:

Pwalk is the probability that a traveler will use walk access to transitPdrive is the probability that a traveler will use drive access to transitK1 is the “nesting coefficient”

The nesting coefficient is a calibrated value that lies in the range 0 to 1. A value of

1 implies that the modes are completely different and that the nesting is not neces-

sary (the modes should compete at a higher level in the nesting structure). A value

of 0 implies that the modes are complete substitutes for each other. In practice, if a

nested mode choice model was rigorously estimated using the “red bus, blue bus”

example from above, the nesting coefficient for the red bus, blue bus nest should be

found to be 0, since there would be no real difference between the buses.

Since the travel data necessary for the rigorous estimation of the mode choice model

did not exist for the region, the mode choice models were transferred from other

areas. Several sources formed the basis for the donor models. The HBW and HBUNIV

mode choice models were originally estimated for the Albuquerque, New Mexico

region based on household and transit survey data that were collected in the early

1990s. In addition to being successfully used in Albuquerque, this model formed

the basis for mode choice models in Bakersfield, California and the Roaring Fork

Valley in Colorado. The other mode choice models were composite models based on

MODE CHOICE


relationships developed by other modelers. These relationships were based on gen-

erally accepted mode choice model principles and experience from regions that have

estimated both work and non-work mode choice models, as follows:

Home-based non-work mode choice models

• The in-vehicle time coefficient should be about one-third of the home-basedwork mode choice model in-vehicle time coefficient,

• The implied value-of-time should be about 20 percent of the home-based workvalue-of-time.

• The out-of-vehicle time coefficient should be about 2.5 times the in-vehicletravel time coefficient.

• The first wait coefficient for wait times over 7.5 minutes should be about 40percent of the out-of-vehicle time coefficient.

Non-home-based mode choice models

• The in-vehicle time coefficient should be about 1.25 times the home-basedwork mode choice model in-vehicle time coefficient.

• The implied value-of-time should be about one-half of the home-based workvalue-of-time.

• The out-of-vehicle time coefficient should be about 2.5 times the in-vehicletravel time coefficient.

• The first wait coefficient for wait times over 7.5 minutes should be about 40percent of the out-of-vehicle time coefficient.

Home-Based Work Model Specification

Specifying the utility equations and model coefficients for the lowest level choices

in the nested logit structure defines the HBW mode choice model. HBW model

coefficients are shown in Table 36. Two sets of model coefficients are shown. The

first is the model coefficients that would be used for an equivalent multinomial logit

model. The second is the actual model coefficients that are used in the utility equa-

tions. As mentioned above, one characteristic of nested logit models is increased

elasticity in lower levels of the nesting structure. The increased elasticity comes

through relatively larger model coefficients at lower levels of the models. The model

coefficients from one level of the nesting structure can be related to equivalent

model coefficients at a different level by multiplying or dividing by the nesting

coefficients. If the coefficients are specified at the top level, they are divided by the

appropriate nesting coefficients to represent them at lower levels of the nesting

structure. If the coefficients are specified at the lowest level, they are multiplied by

the appropriate nesting coefficients for higher levels of the nest. Since models be-

tween different regions are typically compared at the top level (so they can be com-

pared to multinomial model specifications), the top-level specification of the coeffi-

cients is important for documentation purposes.

The utility equations for the HBW mode choice model (lowest level nest) are shown

below. The coefficient designations (e.g., Civtt for Coefficient of in-vehicle travel time)

rather than the actual model coefficients are shown to aid in the understanding of

the model specification. Actual model coefficients are shown in Table 36. Model

constants, Km, calibrated to reproduce observed mode shares in the Fort Collins area

are also shown in Table 36. The walk to premium transit constant was calculated

based on the assumption that if a user had an identical option of using local and

premium transit, 55% of the riders would use premium transit and 45% would use

local transit.

Drive Alone UtilityUDA = Civtt x IVTTDA + Cterm x TTIMEDA + Ccost × (CPM × DISTDA +

PARK8DA / 2)

MODE CHOICE


Shared Ride 2 UtilityUSR2 = Civtt × (IVTTSR2 + FORM2)+ Cterm × TTIMESR2 + [Ccost × (CPM ×

DISTSR2 + PARK8SR2 / 2)] / 2 + Ccbdsr × CBD + KSR2

Shared Ride 3 UtilityUSR3 = Civtt × (IVTTSR3 + FORM3)+ Cterm × TTIMESR3 + [Ccost ×

(CPM × DISTSR3 + PARK8SR3 / 2)] / 3 + Ccbdsr × CBD + KSR3

Shared Ride 4+ UtilityUSR4 = Civtt × (IVTTSR4 + FORM4)+ Cterm × TTIMESR4 + [Ccost ×

(CPM × DISTSR4 + PARK8SR4 / 2)] / 4.1 + Ccbdsr × CBD + KSR4

Transit-Walk Access to LocalUTW-L= Civtt × IVTTTW-L+ Cshort × min(WACCTW-L,9.99) +

Cmedium × min[max(WACCTW-L - 10.0,0),9.99] +Clong × max(WACCTW-L - 19.99,0) +Cshort × min(WEGRTW-L,9.99) +Cmedium × min[max(WEGRTW-L - 10.0,0),9.99] +Clong × max(WEGRTW-L - 19.99,0) +Cswait × min(WAITTW-L,7.5) + Clwait × max(WAITTW-L - 7.5, 0) +Cxfer × XTIMETW-L + Ccost × FARETW-L + Ccbdtw × CBD + KTW-L

Transit-Walk Access to PremiumUTW-P= Civtt × IVTTTW-P+ Cshort × min(WACCTW-P,9.99) +

Cmedium × min[max(WACCTW-P - 10.0,0),9.99] +Clong × max(WACCTW-P - 19.99,0) +Cshort × min(WEGRTW-P,9.99) +Cmedium × min[max(WEGRTW-P - 10.0,0),9.99] +Clong × max(WEGRTW-P - 19.99,0) +Cswait × min(WAITTW-P,7.5) + Clwait × max(WAITTW-P - 7.5, 0) +Cxfer × XTIMETW-P + Ccost × FARETW-P + Ccbdtw × CBD + KTW-P

Transit-Drive Access to Formal Park and RideUTD-F= Civtt × IVTTTD-F + Cdacc × DACCTD-F + Cfpr × FP&R +

Cshort × min(WEGRTD-F,9.99) +Cmedium × min[max(WEGRTD-F - 10.0,0),9.99] +Clong × max(WEGRTD-F - 19.99,0) +Cswait × min(WAITTD-F,7.5) + Clwait × max(WAITTD-F - 7.5, 0) +Cxfer × XTIMETD-F + Ccost × FARETD-F + Ccbdtd × CBD + KTD-F

Transit-Drive Access to Informal Park and RideUTD-I = Civtt × IVTTTD-I + Cdacc × DACCTD-I + Cfpr × FP&R +

Cshort × min(WEGRTD-I,9.99) +Cmedium × min[max(WEGRTD-I - 10.0,0),9.99] +Clong × max(WEGRTD-I - 19.99,0) +Cswait × min(WAITTD-I,7.5) + Clwait × max(WAITTD-I - 7.5, 0) +Cxfer × XTIMETD-I + Ccost × FARETD-I + Ccbdtd × CBD + KTD-I

where: IVTT = In-vehicle travel time in minutes (excluding terminal times)TTIME = Terminal time in minutesCPM = Auto operating cost per mile of travel in cents

= 13.5 cents per mileDIST = Distance traveled in milesPARK8= Daily (8 hour) parking cost in centsFORMx= Carpool formation time for carpool of size “x”

= 1 minute for 2 persons, 2 minutes for 3 persons, and 3.1minutes for 4 or more persons

CBD = CBD attraction zone dummy variable (0/1)WACC = Walk access to transit time in minutesWEGR = Walk egress from transit time in minutesWAIT = “First” wait time for transit in minutesXTIME= Transit transfer time in minutesFARE = Transit fare in centsDACC = Drive access to transit time in minutesFP&R = Formal park and ride lot dummy variable (0/1)Cx = Coefficient for variable “x” (see Tables 36 and 38)Km = Constant for mode “m”

MODE CHOICE


The calibrated constants yield 629 transit riders, compared to 617 observed transit

riders. The desired average auto occupancy was 1.09, and the calibrated result

yielded 1.07 persons per auto.

Table 36: Home-Based Work Mode Choice Model Coefficients

“Top” Level “Bottom” LevelIn-Vehicle Travel Time (Minutes) -0.02087 -0.05963

0-9.99 Minute Walk Time (Minutes) -0.02185 -0.06243Apply to increment with 9.99 maximum

10-19.99 Minute Walk Time (Minutes)

-0.05376 -0.15360Apply to increment with 9.99 maximum

20+ Minute Walk Time (Minutes) -0.14600 -0.41714 Apply to increment

Short Wait Time (<= 7.5 Minutes) -0.09775 -0.27929Apply to increment with 7.5 maximum

Long Wait Time (> 7.5 Minutes) -0.02296 -0.06560 Apply to incrementTransfer Wait Time (Minutes) -0.09775 -0.27929

Drive Access Time (Minutes) -0.14750 -0.42143Apply to drive access sub-modes

Formal Park & Ride (0/1 Dummy) 0.78590 2.24543Highway Terminal Time (Minutes) -0.09775 -0.27929Cost (Cents) -0.00311 -0.00890CBD Attraction-Shared Ride 0.13680 0.39086 Apply to all sub-modes

CBD Attraction-Transit Walk Access 1.15300 3.29429 Apply to all sub-modes

CBD Attraction-Transit Drive Access

-0.05312 -0.15177 Apply to all sub-modes

Access Mode Nesting Coefficient Apply to upper level logsum

Sub-Mode Nesting Coefficient Apply to lower level logsum

Drive Alone Constant 0.00000 0.00000Shared Ride 2 Constant -2.22450 -6.35570Shared Ride 3 Constant -2.59991 -7.42830Shared Ride 4+ Constant -2.57114 -7.34610Walk to Local Transit Constant -2.82545 -8.07270Walk to Premium Transit Constant -2.75520 -7.87200Drive to Formal P&R Constant -5.07427 -14.49790Drive to Informal P&R Constant -5.07427 -14.49790

0.5

CoefficientCoefficient Value

Notes

0.7

As noted in the section describing transit path-building, the walk access and egress

portions of transit skims are stripped out of the skims and replaced with average

times for different market segments being modeled: short walk, medium walk, and

long walk. In effect, nine different walk access-egress markets are defined based on

the proportions of production and attraction zones within the different walk dis-

tance ranges: short-short, short-medium, short-long, medium-short, …, long-long.

For example, suppose 50 percent of a production zone is within short walk access

distance, 30 percent is within medium walk access distance, and 20 percent is

within long walk access distance. For the attraction zone, suppose that 10 percent

is within short walk egress distance, 20 percent is within medium distance, 50

percent is within long, and 20 percent is outside of long egress distance. The pro-

portions of the total zonal interchanges within each of the nine markets are shown

in Table 37.

Table 37: Example Walk Access / Egress Markets

Short Walk (10%)

Medium Walk (20%)

Long Walk (50%)

Outside of Walk (20%)

Total

Short Walk (50%) 5% 10% 25% 10% 50%Medium Walk (30%) 3% 6% 15% 6% 30%Long Walk (20 %) 2% 4% 10% 4% 20%Total 10% 20% 50% 20% 100%

Access MarketEgress Market

In effect, the HBW mode choice model is applied 9 or 10 times for each interchange.

If none of the attraction zone is outside of long walk egress distance, the model can

be applied 9 times to cover 100 percent of the interchange markets. If any portion of

the attraction zone is outside of long walk egress, the model must be applied a 10th

time for “auto only” choices. In the example shown in Table 37, only 80 percent of

the interchanges would have transit options for the interchange. Twenty percent of

the interchanges would have only auto drive alone and shared ride options.

MODE CHOICE


While the home-based work mode choice model must be applied up to 10 times for

each interchange, the application is somewhat simplified due to the mathematics

associated with exponentiation (ea + b = ea x eb).

The utilities represented by “a” include all utilities independent of the access / egress

markets: in-vehicle time, travel cost, terminal times (for auto and shared ride modes),

formal or informal parking for drive access to transit, CBD destinations, etc. The

utilities represented by “b” include all access / egress market dependent utilities.

Thus, the utilities represented by “a” can be calculated once and exponentiated

once for each interchange. The utilities represented by “b” must be calculated and

exponentiated for each market.

Home-Based University Model Specification

The HBUNIV model is based on the HBW model, with the modification of the mode

constants. The utility equations for the HBUNIV mode choice model are the same as

the HBW utility equations shown in the above section. Actual model coefficients are

shown in Table 38. Model constants, Km, calibrated to reproduce observed mode

shares in the Fort Collins area are also shown in Table 38. The calibrated model

results in 2378 transit riders, compared with 2400 observed riders. The average

auto occupancy from the model was 1.24, compared with the desired 1.23 persons

per auto.

Table 38: Home-Based University Mode Choice Model Coefficients

“Top” Level “Bottom” LevelIn-Vehicle Travel Time (Minutes) -0.02087 -0.05963

0-9.99 Minute Walk Time (Minutes) -0.02185 -0.06243Apply to increment with 9.99 maximum

10-19.99 Minute Walk Time (Minutes)

-0.05376 -0.15360Apply to increment with 9.99 maximum

20+ Minute Walk Time (Minutes) -0.14600 -0.41714 Apply to increment

Short Wait Time (<= 7.5 Minutes) -0.09775 -0.27929Apply to increment with 7.5 maximum

Long Wait Time (> 7.5 Minutes) -0.02296 -0.06560 Apply to incrementTransfer Wait Time (Minutes) -0.09775 -0.27929

Drive Access Time (Minutes) -0.14750 -0.42143Apply to drive access sub-modes

Formal Park & Ride (0/1 Dummy) 0.78590 2.24543Highway Terminal Time (Minutes) -0.09775 -0.27929Cost (Cents) -0.00311 -0.00890CBD Attraction-Shared Ride 0.13680 0.39086 Apply to all sub-modes

CBD Attraction-Transit Walk Access 1.15300 3.29429 Apply to all sub-modes

CBD Attraction-Transit Drive Access

-0.05312 -0.15177 Apply to all sub-modes

Access Mode Nesting Coefficient Apply to upper level logsum

Sub-Mode Nesting Coefficient Apply to lower level logsum

Drive Alone Constant 0.00000 0.00000Shared Ride 2 Constant -0.33898 -0.96850Shared Ride 3 Constant -0.85316 -2.43760Shared Ride 4+ Constant -1.04923 -2.99780Walk to Local Transit Constant -0.42354 -1.21010Walk to Premium Transit Constant -0.35329 -1.00940Drive to Formal P&R Constant -5.07427 -14.49790Drive to Informal P&R Constant -5.07427 -14.49790

0.5

CoefficientCoefficient Value

Notes

0.7

MODE CHOICE


Non-Work Mode Choice Models

The assumptions used to specify the non-work mode choice models were listed

above. By definition, the non-work mode choice models are simpler than the HBW

and HBUNIV mode choice model. Utility equations for the non-work models are as

follows:

Auto UtilityUA = Civtt × IVTTA + Cterm × TTIMEA + Ccost × (CPM × DISTA + PARK2A

/ 2) / AAO

Transit UtilityUT = Civtt × IVTTT + Cswait × min(WAITT,7.5) + Clwait × max(WAITT - 7.5,

0) +Cxfer × (XTIMET + WACCT + WEGRT) + Ccost × FARET + KT

where: IVTT = In-vehicle travel time in minutes (excluding terminal times)TTIME = Terminal time in minutesCPM = Auto operating cost per mile of travel in cents

= 13.5 cents per mileDIST = Distance traveled in milesPARK2= Two-hour parking cost in centsAAO = Average home-based non-work or non-home-based

average auto occupancyWACC = Walk access to transit time in minutesWEGR = Walk egress from transit time in minutesWAIT = Wait time for transit in minutesXTIME= Transit transfer time in minutesFARE = Transit fare in centsCx = Coefficient for variable “x” (see Table 39)Km = Constant for transit mode

Detailed walk access/egress market segmentation is not performed for the non-

work mode choice models.

Table 39 shoes the coefficients used in the non-work mode choice models as well as

a comparison of the coefficients used in the HBW and HBUNIV models. The models

compare very well to the observed transit counts taken in the onboard transit sur-

vey. HBSHOP yielded 160 transit trips compared with the desired 160 transit trips.

HBO matched the observed 491 transit trips exactly. WBO modeled 118 transit trips

compared with 119 observed transit trips. OBO resulted in 281 transit trips com-

pared with 280 transit trips.

Table 39: Non-Work Mode Choice Model Coefficients

Coefficient HBW1 HBUNIV1 HBSHOP HBO WBO OBOIn-Vehicle Travel Time (Minutes) -0.020870 -0.020870 -0.006960 -0.006960 -0.026090 -0.026090Out-of-Vehicle Travel Time (Minutes)2 -0.097750 -0.097750 -0.017400 -0.017400 -0.065230 -0.065230First Wait (<= 7.5 Minutes) -0.097750 -0.097750 -0.017400 -0.017400 -0.065230 -0.065230First Wait (> 7.5 Minutes) -0.022960 -0.022960 -0.006960 -0.006960 -0.026090 -0.026090Transfer Wait -0.097750 -0.097750 -0.017400 -0.017400 -0.065230 -0.065230Cost -0.003114 -0.003114 -0.005220 -0.005220 -0.007790 -0.007790Auto Constant n/a n/a 0 0 0 0Transit Constant n/a n/a -4.703 -4.7297 -3.7216 -3.5482Implied Value of Time1 $4.02 / hour $4.02 / hour $0.80 / hour $0.80 / hour $2.01 / hour $2.01 / hour1 Information shown for comparison purposes.2 Out-of-vehicle time includes terminal time for auto and walk access, walk egress, and transfer time for transit.

Home-Based Shopping and Home-Based Other Auto Occupancy Models

The non-work mode choice models produce only estimates of the numbers of per-

son trips made in autos. In order to determine the number of vehicle trips, average

auto occupancies must be estimated. The original plan for the Phase 2 model up-

date was to develop a home-based non-work average auto occupancy model based

on average household sizes and income levels in the production (home) zones. The

model was to have been estimated using data reported in the 1998 North Front

Range Mobility Report Card survey. However, it was discovered that the socioeco-

nomic data maintained for the North Front Range does not include estimates and

forecasts of both households and population on a zonal basis. Thus, while such a

model could have been developed, it would not have been possible to apply the

model. As a result, the Phase 3 model continues to use the home-based non-work

average auto occupancies developed for the MinUTP model. The average auto occu-

pancies vary by zone and, theoretically, reflect the same variation that would have

been reflected by the planned model.

MODE CHOICE


Work-Based Other Auto Occupancy Models

Non-home-based average auto occupancies are estimated using an alternative pro-

cedure. Since the actual locations of non-home-based trip ends are not necessarily

related to the home locations of the travelers, the home-based non-work procedures

are not valid. Non-home-based average auto occupancies are related to the number

of people (potential travelers) and the number of vehicles available in the zones

where non-home-based trips actually originate. The relationship is straightforward–

the more potential travelers there are per vehicle, the higher the non-home-based

average auto occupancy.

The model was transferred from the Colorado Springs region and calibrated to match

regional WBO average auto occupancies for the North Front Range region. The

Colorado Springs region estimated “regression-based” models for both non-home-

based work-related and non-home-based other trip purposes. The model was cali-

brated to match the overall regional WBO average auto occupancy by modifying the

constant. The desired WBO average auto occupancy was 1.22 which was matched

exactly by the base year 1998 Mason Street model. The following model resulted:

WBOAAO = 0.32 + 0.82 * PPAARRwhere:

WBOAAO = the work-based other average auto occupancyPPAARR = the persons per auto arriving in an attraction zone.

The number of persons per auto arriving at each zone is determined from the results

of the person trip distributions of HBW trips combined with the results of the home-

based non-motorized mode split and motorized mode choice models, and the home-

based average auto occupancy model. In effect, the column sums of the HBW per-

son trip matrix provide the persons arriving in each zone, and the column sums of

the HBW vehicle trip matrix provide the number of autos arriving in each zone.

Other-Based Other Auto Occupancy Models

Other-based other average auto occupancies are estimated using the same proce-

dure as the WBO average auto occupancy model.

The model was transferred from the Colorado Springs region and calibrated to match

regional OBO average auto occupancies for the North Front Range region. The model

was calibrated to match the overall regional OBO average auto occupancy by modi-

fying the constant. The desired OBO average auto occupancy was 1.65. The mod-

eled result was 1.66 for the base year 1998 model. The following model resulted:

OBOAAO = 0.35 + 0.84 * PPAARRwhere:

OBOAAO = the other-based other average auto occupancyPPAARR = the persons per auto arriving in an attraction zone.

The number of persons per auto arriving at each zone is determined from the results

of the person trip distributions of HBUNIV, HBSHOP, and HBO trips combined with

the results of the home-based non-motorized mode split and motorized mode choice

models, and the home-based average auto occupancy model. In effect, the column

sums of the HBUNIV, HBSHOP, and HBO person trip matrices provide the persons

arriving in each zone, and the column sums of the HBUNIV, HBSHOP, and HBO

vehicle trip matrices provide the number of autos arriving in each zone.

MODE CHOICE


Time-of-Day Traffic AssignmentThe traffic assignment module loads vehicle trips onto the roadway network to

estimate link-specific traffic volumes. As part of the enhancements made to the

model, a time-of-day capability has been incorporated so that more accurate estimates

of diurnal travel patterns and air quality results can be determined.

The time-of-day assignment process uses the vehicle trip table in production-

attraction format from the mode split/choice model and converts it into four time

periods: AM peak, midday peak, PM peak, and off-peak periods. Each of these four

trip tables are assigned in four time-of-day assignment procedures with period-

specific speeds and capacities. The resulting traffic volumes from the four assignments

can be summed to estimate a 24-hour volume for each link in the network.

Time-of-Day AnalysisDetermination of Time Periods

Intervals were developed to model four time periods throughout the day at the

request of the City of Fort Collins. They include morning, midday, and evening peak

periods and an off-peak period. Through the coordination of model development

activities with the City and metropolitan planning organization, it was agreed that

the time periods would not be contiguous so that distinct peaking characteristics

could be modeled explicitly. In other words, the off-peak period includes the gaps

between the morning and midday peaks, the midday and evening peaks, and the

nighttime period between the evening and morning peaks. Furthermore, the model

coordination group indicated a desire to have time periods in hourly intervals.

The resulting peak periods were developed as follows:

Peak Period Interval Duration (hours)Morning 6:30 a.m. to 8:29 a.m. 2Midday 11:30 a.m. to 1:29 p.m. 2Evening 3:30 p.m. to 6:29 p.m. 3

The off-peak period comprises the times not covered by a peak period.

The 1998 Mobility Report Card household survey data was used to determine the

peak period definitions. Trip records were weighted by jurisdiction and expanded

by household size and income to reflect the daily trips in the region. The analysis

considered only the auto driver, carpool driver, vanpool driver, and motorcycle trips

from the 1998 MRC household survey data. For this analysis, the trips were also

weighted by a surrogate for VMT, the reported trip length in minutes factored by an

average speed of 30 mph, to simulate trip distance.

The analysis of time period intervals was initiated by selecting the 30-minute bin

with the highest occurrence of trips from mid-point time distribution. Next, the

highest two-hour interval around the highest 30-minute bin was determined for

each peak period. For the evening peak period, both two and three-hour intervals

were analyzed due to the high number of trips occurring between 3:00 and 4:00

pm. These trips are likely due in large part to school trips.

Results are shown in Table 40 and Figure 15. The midday peak period is less distinct

than may have otherwise occurred with an analysis not weighted by VMT due to

shorter trip lengths and increased non-motorized travel modes during the midday.

Likewise, the morning and evening peaks are more pronounced.

TIME OF DAY TRAFFIC ASSIGNMENT


Table 40: 1998 MRC WeightedExpanded Vehicle Trips

Time Mid-pointmidnight - 12:29 am 0.0%

12:30 - 12:59 0.0%1:00 - 1:29 0.1%1:30 - 1:59 0.1%2:00 - 2:29 0.0%2:30 - 2:59 0.0%3:00 - 3:29 0.0%3:30 - 3:59 0.1%4:00 - 4:29 0.3%4:30 - 4:59 0.3%5:00 - 5:29 0.4%5:30 - 5:59 1.5%6:00 - 6:29 1.5%6:30 - 6:59 3.2%7:00 - 7:29 4.1%7:30 - 7:59 4.3%8:00 - 8:29 2.9%8:30 - 8:59 2.5%9:00 - 9:29 2.3%9:30 - 9:59 2.8%

10:00 - 10:29 2.6%10:30 - 10:59 2.2%11:00 - 11:29 2.7%11:30 - 11:59 2.8%

Noon - 12:29 pm 3.1%12:30 - 12:59 3.5%

1:00 - 1:29 3.1%1:30 - 1:59 2.9%2:00 - 2:29 2.9%2:30 - 2:59 3.2%3:00 - 3:29 3.0%3:30 - 3:59 4.1%4:00 - 4:29 3.8%4:30 - 4:59 5.4%5:00 - 5:29 5.5%5:30 - 5:59 4.8%6:00 - 6:29 3.6%6:30 - 6:59 3.0%7:00 - 7:29 2.4%7:30 - 7:59 1.5%8:00 - 8:29 1.5%8:30 - 8:59 1.0%9:00 - 9:29 1.5%9:30 - 9:59 1.2%

10:00 - 10:29 1.2%10:30 - 10:59 0.4%11:00 - 11:29 0.4%

11:30 - 11:59 pm 0.1%

Figure 15: Diurnal Distribution for 1998 MRC Weighted and Expanded Vehicle Trips

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

midnightnoonmidnight

Results indicate that morning peaking occurs in the two-hour time period from 6:30 to 8:29 am. The midday peaking occurs

between 12:00 noon and 1:59 pm; however, an almost identical amount of peaking happens between 11:30 am and 1:29 pm. The

City indicated the latter interval would more appropriately suit their needs. The evening peak period results provide sufficient

evidence for either a two-hour or three-hour interval. The latter was selected by the City due in part to include the mini school peak

from 3:30 to 4:00. Therefore, the evening peak period interval occurS between 3:30 and 6:29 pm.

Time-of-Day Factors by Trip Purpose

The time-of-day process was developed for the Mason Street Multi-modal Travel Model to run prior to the traffic assignment module.



While the aggregate analysis of all vehicle trips serves to define the peak periods

used in the model, the time-of-day factors are applied to the production-attraction

(P-A) trip tables by trip purpose. Directional splits (e.g., home to work vs. work to

home) were determined as part of this process, so that P-A and A-P factors could be

applied appropriately.

The analysis of purpose specific time-of-day factors uses the vehicle trips from the

1998 Mobility Report Card survey weighted by jurisdiction and expanded to simulate

regional trip-making. These trips are not weighted by VMT. The trip mid-point times

were utilized to place trips into 30-minute bins for aggregation into the three peak

time periods and the off-peak period as defined above. The trips were not smoothed

subsequent to their aggregation into the bins.

The 1998 MRC data did not contain sufficient trip records for determining the HBSHOP

time-of-day factors in the A-P direction. To compensate, a pivot point analysis was

conducted using the A-P and P-A time-of-day factor relationships for HBO trips.

These ratios were applied by time period to the HBSHOP P-A factors to develop

HBSHOP A-P factors.

For the HBUNIV trip purpose, the 1999 CSU Special Generator Study was utilized

for P-A time-of-day factors. A cordon line count was taken during the study that

provided person arrival times throughout an entire school day. Although specific

trip purposes could not be distinguished from the cordon count, these trips are

dominated by the HBUNIV trip purpose and, therefore, were applied to provide

reasonable HBUNIV time-of-day P-A factors. The CSU cordon count was recorded

for hourly intervals, so the two highest morning and midday hours were used for

the morning and midday peak periods. Likewise, the three highest evening hours

were used for the evening peak. Because the cordon study counted arrivals, or trip

end times, and the time-of-day analysis is based on mid-point trip times, this

approach was considered reasonable. Since the cordon study only counted arrivals

(P-A trips), the departures (A-P trips) were estimated using a pivot point analysis

based on a 1993 cordon count of the University.

No local survey data was available to provide directional time-of-day factors for

internal/external (I/E) and external/external (E/E) trips. However, detailed traffic

count data was available for the three external stations along the region’s southern

boundary with the Denver metropolitan area. This information yielded directional

time-of-day factors that showed traffic movements indicating the North Front Range

is a net exporter of trips to jobs in Denver. For example, in the P-A direction (i.e.,

northbound from Denver), the evening peak period movement was much higher

than the morning peak, indicating that commuters are returning in the evening to

their homes in the North Front Range. The A-P (southbound) direction showed

similar results but are somewhat misleading given the two-hour morning peak

period as compared to the three-hour interval in the evening. The results in the A-P

direction are also affected by the overall increase in trip-making in the evening peak

period as compared to the morning peak.

Although the North Front Range region tends to export workers to Denver, it is

generally understood that the region is a net importer of work trips from areas to

the west, north, and east. For example, there seems to be a fair number of people

commuting from their homes in Cheyenne, Wyoming to work each day in northern

Colorado. Therefore, for those external stations along the western, northern, and

eastern boundaries of the region, the directional time-of-day factors for I/E trips are

reversed so that there is a net influx of trips in the morning and a corresponding

egress of trips in the evening for those locations. The southern boundary I/E time-

of-day factors were reversed to estimate the factors for the other external stations.

External travel in the model is made up of I/E trips (93%) and E/E trips (7%). Since

E/E trips make up such a small part of the external travel, the directional time-of-



day factors calculated for I/E trips were applied to the E/E trips in O-D format. The

resulting E/E time-of-day factors for external trips represent a simple averaging of

the directional I/E factors.

The resulting time-of-day directional factors are shown in Table 41. In the travel

model, the P-A factors are applied directly to the purpose-specific P-A vehicle trip

tables produced by the mode choice module to obtain P-A directional trips by purpose

and time period. To apply the A-P factors, the P-A vehicle trip tables from mode

choice must first be transposed to the proper format and multiplied by the A-P

factors to obtain A-P directional trips by purpose and time period. Then, the resulting

P-A and A-P tables are summed across all trip purposes by time period for loading in

the four time-of-day traffic assignment modules. The trips summed across all trip

purposes, times-of-day, and direction (P-A, A-P) equal the number of trips in the

original P-A vehicle trip table from mode choice. Trip tables in O-D format utilize the

time-of-day factors in the same manner.

Table 41: Time-of-Day Directional FactorsMorning Peak Midday Peak Evening Peak

Period Period Period Off-Peak(6:30 - 8:29 am) (11:30 am - 1:29 pm) (3:30 - 6:30 pm) Period Total

HBWP to A 0.291 0.040 0.018 0.194 0.543A to P 0.009 0.056 0.254 0.138 0.457HBUNIVP to A 0.109 0.074 0.086 0.231 0.500A to P 0.025 0.071 0.152 0.252 0.500HBSHOPP to A 0.105 0.051 0.100 0.237 0.493A to P 0.016 0.062 0.153 0.276 0.507HBOP to A 0.105 0.051 0.100 0.237 0.493A to P 0.016 0.062 0.153 0.276 0.507WBOP to A 0.017 0.121 0.160 0.184 0.482A to P 0.094 0.132 0.056 0.236 0.518OBO (O-D Format)O to D 0.042 0.182 0.240 0.536 1.000IE Trips (to Denver)*P to A 0.051 0.051 0.136 0.262 0.500A to P 0.082 0.051 0.098 0.269 0.500IE Trips (all others)**P to A 0.082 0.051 0.098 0.269 0.500A to P 0.051 0.051 0.136 0.262 0.500EE Trips (O-D Format)***O to D 0.134 0.102 0.234 0.530 1.000

* Southern boundary stations: 299 (US287), 300 (I25), and 301 (US85)** Applied to external stations along western, northern, and eastern boundaries

*** Non-directional E/E factors based on average of I/E directional t.o.d. trip factors

Traffic AssignmentThe four traffic assignment modules assign vehicle trips to the roadway network to

estimate link-specific traffic volumes. Generally, the program reads the time-of-day

trip table matrix, builds the shortest paths between zone pairs, assigns trips to the

links in the paths, adjusts the link travel times based on congestion, and repeats

the process for a specified number of iterations. In the TransCAD-based Mason

Street model, the peak period assignments utilize user equilibrium assignment

processes and the off-peak period uses a stochastic process. User equilibrium is an

iterative process that achieves a convergent solution in which no traveler can improve

his/her travel time by shifting routes. The stochastic assignment distributes trips

between zones among multiple reasonable paths in which the link travel times are

fixed and not dependent on congestion.



For the equilibrium assignments that are conducted for the three peak time periods,

the following volume-delay equation is used:

t = tf * [1 + α(v/c)β]

where:t =congested link travel timetf =initial free-flow link travel timev =link volumec =time-of-day link capacities

α,β =calibration parameters

The volume-delay equation above is a Bureau of Public Roads (BPR) formulation

that continues to be one of the most commonly used link performance functions in

the travel models across the country. Based on reasonable results in the 1998

validation year, the original MinUTP volume-delay parameters continue to be used

in the TransCAD-based Mason Street model. They are shown in Table 42.

Table 42: BPR Parameters

Alpha BetaFreeway 0.6 4.0Arterial 0.5 4.0

In order to implement time-of-day assignments, corresponding link capacities must

be estimated. This was done by developing period capacity multipliers for each time

period. The multipliers were calculated as the VMT for the time period divided by

the VMT for the highest contiguous one-hour period within the time period. The

resulting multiplier is then multiplied by the hourly link capacity to determine period

capacity. The multipliers are as follows:

• 1.73 for the two-hour AM peak period

• 1.89 for the two-hour midday peak period

• 2.50 for the three-hour PM peak period

• 7.39 for the 17-hour off-peak period



Two of the primary benefits of incorporating the time-of-day methodology in trip

distribution and traffic assignment are (1) to more appropriately distribute trips

based on the real-world speeds present in the system when the trips occur and (2)

to provide more accurate estimates of mobile source emissions. To support these

objectives, accurate speeds are necessary. To this end, a speed survey was conducted

as part of the model enhancement effort, new speed estimation techniques were

developed, and a speed feedback loop was incorporated into the modeling process.

1998 Observed SpeedsA speed survey was conducted on many of the more congested roadways in the

regional transportation network to provide an estimate of free-flow and congested

speeds. The segments surveyed were chosen from regional functional and area type

strata. Because resources were not available to conduct a random sample, a prioritized

sampling procedure was employed that focused on 10 to 20 percent of the most

heavily congested segments in the region. At least one segment from each functional

type and area type was surveyed.

A hybrid floating car/average car technique was used to gather travel time information

on the selected segments. Times were recorded using a laptop computer programmed

to record the time automatically in an Excel spreadsheet. Each time interval was

recorded at the far side of the intersection upon which the segments were based. In

other words, as the test vehicle approached an intersection, the time was recorded

after the vehicle proceeded through it. In this manner, the intersection delay was

included in the time interval. While some limited speed data was available from the

local jurisdictions, they utilized a spot speed technique that does not capture the

intersection delay.

Several survey runs were conducted for each segment. At least one run was conducted

in both the peak and off-peak periods for each segment. For this exercise, the peak

periods were defined from 7:30 to 8:30 AM and 4:00 to 6:00 PM. Off-peak runs

were conducted outside of these time periods between 7:00 AM and 6:30 PM. After

editing and cleaning the data, distance-weighted averages were calculated for peak

and off-peak speeds by functional type and area type. Distance was used to weight

the speeds rather than volume because traffic counts were not recorded during the

speed survey.

Freeway, collector streets, and ramps were not surveyed. The estimated free-flow

and congested speeds used as input to the model are based on speeds used in the

original MinUTP model with minor adjustments made by the model coordination

committee made up of City, MPO, and consultant staffs.

The off-peak and peak observed speeds are shown in Tables 43 and 44 respectively.

Peak speeds were used as input to the travel model as estimated congested speeds.

As discussed later, these speeds are eventually replaced through the speed feedback

process. Off-peak speeds were likewise used as estimates for free-flow speeds. The

free-flow speeds are replaced by the feedback loop similar to the peak speeds, but

the estimated input free-flow speeds remain in the networks for use in traffic

assignment and the speed estimation process.

Speed Estimation and Feedback

SPEED ESTIMATION AND FEEDBACK


Table 43: Estimated Free-flow (Off-Peak) Speeds (mph)

Freeway ExpresswayMajor

ArterialMinor

Arterial CollectorFreeway Ramp

Urban 78 40 34 30 25 30Rural 78 65 57 48 35 30CBD n/a n/a 26 17 15 n/a

Table 44: Estimated Congested (Peak) Speeds (mph)

Freeway ExpresswayMajor

ArterialMinor

Arterial CollectorFreeway Ramp

Urban 75 35 32 27 25 30Rural 75 65 54 47 35 30CBD n/a n/a 18 15 15 n/a

Speed EstimationSpeed estimation is conducted using the modeled volumes, theoretical capacity

recorded on each link, and a mathematical relationship that factors the free-flow

speed based on the volume and capacity. In fact, the same BPR volume-delay equation

that is used to model congestion effects in the traffic assignment module is applied

here with different calibration parameters. Ideally, the parameters for both processes

should be the same due to the interrelationship of volume, capacity, and travel

time. However, a detailed evaluation of network capacities would be necessary for

this to occur. Furthermore, the stochastic assignment for the off-peak period does

not consider any congestion effects, so any resulting speed estimation would be

suspect. Finally, there are numerous models across the country that have separate

calibration parameters for the two processes, so the technique is considered acceptable

modeling practice. Thus, in an effort to provide the most accurate speeds possible

from the model, new calibration parameters have been calibrated. They are shown

in Table 45.

Table 45: BPR Parameters for Speed Estimation

Alpha BetaUrban Freeways 0.60 4.0Urban Arterials 1.50 4.0Rural Freeways 0.45 4.0Rural Arterials 0.32 6.0CBD Freeways n/a n/aCBD Arterials 1.78 2.4

Speed FeedbackA speed feedback loop has been incorporated into the modeling process in order to

ensure consistency of speeds. This corrects a fundamental problem with travel models

that occurs when estimated speeds used in the trip distribution process are not the

same as those that result from the traffic assignment/speed estimation process.

The feedback loop is conducted for both free-flow and congested speeds. For free-

flow speeds, the speeds estimated from the off-peak traffic assignment/speed

estimation model are fed back to the top of the modeling chain and used in the off-

peak trip distribution model. In this case, the original estimated input free-flow

speeds are not removed from the network file, because they are used as a starting

point for each of the four traffic assignments and their associated speed estimation

processes. The feedback loop is iterated until the difference between the input speeds

(i.e., the output speeds from the previous model run) and the output speeds estimated

after traffic assignment reaches an acceptable minimum. For the Mason Street model,

this occurs when the speed difference for each functional type and area type strata

is less than 1.0 mile per hour.

The congested speeds are fed back in a similar manner, except that the original

input estimated congested speeds are overwritten in the process because they are

no longer necessary in the remainder of the modeling chain. The morning peak



period speeds are fed back to trip distribution in this case instead of the evening

peak period speeds due to the production-attraction orientation of HBW and HBUNIV

trips in trip distribution. Closure criteria for the congested speeds is the same as that

for the free-flow speeds.

The networks delivered with the 1998 and 2020 models contain speeds that have

been fed back such that closure has been attained. Generally, this takes between

two and three model runs, or iterations, to reach. For future model runs, the feedback

loop is not necessary if the network changes won’t significantly affect trip distribution

patterns. This is generally the case for projects such as transit improvements, roadway

widenings, and minor roadway extensions/new roadways. For major new facilities

or significant upgrading of an existing facility, feedback may be necessary.

The final speeds resulting from the speed feedback process vary by year due to the

additional traffic and higher volume-to-capacity ratios in 2020. These speeds are

presented in Tables 46 and 47.

Table 46: 1998 Final Speeds after Feedback

Urban Rural CBD Urban Rural CBDFreeway 76.6 72.7 n/a 71.7 67.9 n/aExpressway 38.5 64.8 n/a 31.2 63.4 n/aMajor Arterial 30.8 53.9 20.4 26.3 49.9 17.6Minor Arterial 27.9 47.7 14.1 24.5 46.0 12.8Collector 21.4 34.9 13.6 21.4 34.1 12.6

Free-Flow Congested

Table 47: 2020 Final Speeds after Feedback

Urban Rural CBD Urban Rural CBDFreeway 52.2 49.6 n/a 49.4 51.1 n/aExpressway 31.6 52.3 n/a 27.9 55.1 n/aMajor Arterial 27.3 41.3 17.7 23.7 41.2 15.2Minor Arterial 24.5 43.4 14.7 23.5 39.9 14.3Collector 17.5 33.5 12.4 18.1 28.7 11.3

Free-Flow Congested



Validation involves testing the model’s predictive capabilities. Travel models must

reasonably be able to replicate observed conditions before being used to produce

reliable future-year forecasts. Since the Mason Street Multi-modal Travel Demand

Model has been almost completely redeveloped as compared to its MinUTP origins,

a validation effort is in order.

The validation process relies on observed data for comparison purposes. In many

cases, this data is not available explicitly, but can be derived through analysis of

other regions of similar size and character. In addition, the observed data that is

available for validation is often the same data used to develop and calibrate the

model. In these instances, the validation effort becomes a self-fulfilling prophesy

and additional information should be utilized to ensure the observed data is

reasonable and accurate.

Many of the validation tests conducted for the 1998 Mason Street Multi-modal

Travel Demand Model have been described and demonstrated in the previous

chapters. For these reasons, this section focuses on the validation of the traffic

assignment module through the basic test of comparing roadway volumes to observed

traffic counts. In some cases, the validation tests are based on the Model Validation

and Reasonableness Checking Manual, published by the Travel Model Improvement

Program of the Federal Highway Administration in February 1997.

The reader is reminded that the focus of the validation effort was for the City of Fort

Collins and the Mason Street Transportation Corridor study area. While the overall

model results look very encouraging from a validation standpoint, additional work

is necessary for reliable forecasting in other areas.

Vehicle Miles of TravelAn independent regional or sub-regional estimate of vehicle miles of travel was not

available for this effort, so the validation of VMT relies on the comparison of modeled

volumes with observed traffic counts. The traffic counts have been adjusted for

trucks and count year as described in Chapter 2. The comparison is shown in Tables

48 and 49. The VMT indicated in these tables is based on only those roadway links

in which an observed traffic count has been recorded in the network file.

Table 48: 1998 VMT by Functional Type and JurisdictionVMT From

ModelVMT From

Traffic CountsPercent

DifferenceMaximum

Desirable ErrorFreeways 1,086,275 1,004,663 8% 20%Expressways 357,607 347,418 3% 25%Major Arterials 1,329,824 1,351,020 -2% 20-30%Minor Arterials 646,818 674,122 -4% 30-40%Collectors 144,375 193,451 -25% 40-50%

Table 49: 1998 VMT by JurisdictionVMT From

ModelVMT From

Traffic CountsPercent

DifferenceFort Collins 789,383 798,014 -1%Loveland 783,937 787,304 0%Greeley 768,244 743,662 3%Other/Rural 1,223,651 1,242,626 -2%Region 3,565,215 3,571,606 0%

The maximum desirable error values were estimated from the National Cooperative

Highway Research Program (NCHRP) Report 255, Highway Traffic Data for Urbanized

Area Project Planning and Design. The validation of VMT by functional type appears

very reasonable since all of the values fall well within the error limits. Furthermore, the

VMT validation by jurisdiction is even more encouraging since it appears that the model

reasonably predicts travel in each of the major jurisdictions and across the region.

1998 Model Validation

1998 MODEL VALIDATION


ScreenlinesAnother important validation test is the screenline comparison. Screenlines are

imaginary lines that extend across a series of roadway links that form a logical

basis for evaluation of significant travel movements. For example, physical barriers

such as a river or railroad often make for good screenline definitions. Jurisdictional

boundaries also make excellent screenlines, especially if the jurisdictions are

sufficiently separated to measure travel between them. The three major cities in the

North Front Range are exceptionally well-suited for screenline analysis due to their

physical separation.

One particularly important aspect of screenline analysis is the need for the collection

of observed traffic counts on every link that the screenline crosses. In the case of the

Mason Street model, this criteria was not fulfilled and a surrogate procedure was

adopted wherein some of the traffic counts on lower volume roadways were estimated

as 1000 vehicles per day. The screenlines used in the original MinUTP model were

utilized in this analysis because the process of defining new screenlines requires

that additional traffic counts be collected. The screenlines are shown in Figure 16.

Figure 16: Regional Screenlines

The screenline comparison is shown in Table 50.

Table 50: 1998 Screenline Analysis

ScreenlineSum of Model

FlowsSum of Traffic

CountsPercent

Difference

Maximum Desirable

ErrorA 44,118 44,356 0% 33%B 108,533 113,022 -4% 23%C 169,435 157,889 7% 18%D 135,284 126,022 7% 21%E 63,879 39,438 62% 37%F 66,876 76,258 -12% 27%

The screenline analysis is encouraging because all of the screenline in the vicinity

of the City of Fort Collins show acceptable results. In fact, all of the screenline values

are well within acceptable error limits with the exception of Screenline E, which

runs north and south between Greeley and I-25. Several reasons may explain this



problem. First, there is very limited count coverage on this screenline, and the counts

that have been collected should be reviewed for reasonability. Second, this area was

not the focus of the Mason Street model enhancements, so the cause and solution

to Screenline E should be examined through a regional effort. Third, several traffic

analysis zones in Greeley showed some need for additional review and update. This

may be affecting the allocation of travel within Greeley. Finally, the trip generation

process indicates a high amount of attractions in Greeley compared to the productions

for some purposes. This means that trip distribution must allocate productions

from outside Greeley to satisfy the attraction demand there. Whether or not this is

the case in the real-world has not been determined but should be a topic of future

research through the regional modeling process.



A detailed performance module has been developed as part of the Mason Street

model enhancement effort to report various performance aspects of the model and

the transportation system under evaluation. Example performance reports for the

1998, 2020 Build, and 2020 No-Build Mason scenarios are contained in the Appen-

dix.

The following summaries are available in the performance module. Each section

below gives a brief description of what calculations are reported.

File Name SummaryThis summary lists all of the files used in the current model run.

Socioeconomic Data for the RegionThis summary displays the Fratared socioeconomic data created in the model by

household size and income group for the region and for each of 10 jurisdictions.

There is also a summary of total households, retail employment, nonretail employ-

ment, and total employment for each of 10 jurisdictions and the region. This data is

also summarized by the three major cities in the region; Fort Collins, Greeley, and

Loveland.

Socioeconomic Data for Fort CollinsThis summary displays the same information as the previous report, but summa-

rizing the data in the Fort Collins area in the following way: total Fort Collins,

Mason Street Corridor, Colorado State University, and the central business district.

These areas are not mutually exclusive. In other words, the area definitions overlap.

Trip Generation for the RegionFor each of 10 jurisdictions and the region the following are summarized.

• productions by purpose

• attractions by purpose

• productionS per household by purpose

• home-based work trip productions per employee

• home-based work trip attractions per employee

Trip Generation for Fort CollinsThe same summaries as above are reported in the four Fort Collins areas.

Trip DistributionAverage trip lengths for the region are reported by purpose in time (minutes) and

length (miles). The average speed is also reported by purpose. Interzonal, Intra-

zonal, and total trips are reported by purpose for the region.

Bike and Pedestrian Trips for the RegionThe number of productions and attractions made by bicycle and pedestrian modes

Performance Module

PERFORMANCE MODULE


are reported by the 10 jurisdictions and the region. These values are also summa-

rized by the three main cities.

Bike and Pedestrian Trips for Fort CollinsThis summary reports the number of trips that have BOTH origins and destinations

within the four Fort Collins areas and the number of trips produced in the four Fort

Collins areas.

Mode Split and Mode Choice for the RegionThe following summaries are included for the 10 jurisdictions and the region:

• Mode Split transit trip productions and attractions (does not include zones withaccess to the Fort Collins transit system).

• Mode Choice transit trip productions and attractions (includes only the zoneswith access to the Fort Collins transit system).

• Total transit combines Mode Split transit trips and Mode Choice transit trips.

• Home-based Work and Home-based University detailed mode shares for themode choice portion of the region.

Vehicle Trips Assigned for the RegionFor each time period, the intrazonal, interzonal, and total trips in production/attrac-

tion format and attraction/production format are listed for each trip purpose. These

are then summarized for daily trips as well.

Validation and Screenlines for the RegionThere is a one-page summary for each of the 10 jurisdictions and the region con-

taining the vehicle miles traveled on links with counts by facility type and area

type, the count vehicle miles traveled on links with counts by facility type and area

type, and the ratio of the two (to compare the modeled VMT and the count VMT

facility type and area type).

The screenline summary includes the sum of daily counts, the sum of assigned

volumes on each of the six screenlines, and the ratio of the two. It is also separated

by freeway links and non-freeway links.

VMT Summary for the RegionThe VMT Summary contains the total vehicle miles traveled and the vehicle hours

traveled for the region.

VMT Summary for Fort CollinsThe VMT Summary contains the total vehicle miles traveled and the vehicle hours

traveled for Fort Collins and the Mason Street Corridor.

Speeds, VHT, and Congestion for the RegionThe first page of this summary reports the VMT weighted average input congested

and free-flow speeds by facility type and area type. The following pages report the

VMT-weighted-average loaded congested speeds, the average volume-to-capacity

ratio based on the level of service “C” capacities, and the regional vehicle miles

traveled by facility type and area type and for each time period.

Emissions for the RegionCarbon monoxide, volatile organic compound, and nitrogen oxide emissions are

calculated for each time period and summed to obtain the daily emissions. These

PERFORMANCE MODULE


calculations are performed on a link by link basis and summed for each facility type

and area type combination.

The model allows four emission factor files to be referenced by the performance

module, one for each of the four time-of-day periods. The performance model, as

delivered, includes a year 2020 emission factor file for a 24-hour period since time-

of-day specific parameters were not available.

EPA’s most recent emission factor model, MOBILE5b, provides emission factors in

grams per vehicle mile. These factors are multiplied by the vehicle miles of travel

from the Mason Street travel model. Emission factors are applied as a function of

vehicle speed from the travel model. The resulting on-road mobile source emissions

are then converted from grams to tons.

The MOBILE5b emission factor development and the emissions estimation process

were coordinated with the City of Fort Collins Department of Natural Resources and

the Colorado Department of Health’s Air Pollution Control Division.

MOBILE5b input files were developed with the most recent information available.

Local inspection/maintenance and anti-tampering programs are modeled. Likewise,

local Reid Vapor Pressure, temperature, oxygenated fuel, operating mode start frac-

tions, and vehicle registration data were incorporated. However, only average daily

temperatures were available. This is suitable for system-level analysis, but a more

rigorous analysis should include the development of time-of-day emission factors

based on period-specific temperatures and other parameters.

Emission factors were developed for year 2020 winter and summer scenarios. The

winter scenario yields carbon monoxide factors, consistent with air quality

exceedences and control strategies associated with the winter months. With the

growing summer concern of ozone-related smog, estimates of volatile organic com-

pounds (VOC) and nitrogen oxides (NOX) have been developed. These are based on

the summer MOBILE5b scenario. VOCs and NOXs combine in the presence of sun-

light and form the pollutant ozone.

MOBILE5b input values were adjusted for differences between summer and winter

accordingly. Reid Vapor Pressure and temperatures were changes, and the oxygen-

ated fuels credits are removed from the summer scenario.

Emissions for Fort CollinsThe same calculations are performed as for the region, but for the Fort Collins

Nonattainment Area.

PERFORMANCE MODULE


Scenario ManagerThe scenario manager is used as a means to organize model run input and output

files. The scenario manager is accessed using the “Setup Scenarios” button on the

model dialog box. The model uses all of the files for each scenario for a model run.

The performance module will provide a summary of all files used in the model run.

Scenario Manager FilesWhen a scenario is created, TransCAD creates several files with the extension of

ARR. The file naming convention is as follows:

Scenario #1.ARR – Description and date the scenario was last modified

Scenario #2.ARR – Geographic and network files

Scenario #3.ARR – Trip generation files

Scenario #4.ARR – Trip distribution files

Scenario #5.ARR – Mode split, choice, assignment, and emission files

Scenario #6.ARR – All output files

Scenario #params.ARR – Parameters for mode choice

Where # = the scenario number from the scenario manager.

These files can be manipulated in a text editor, but be careful that there are no extra

lines or spaces at the end, as it will not be understood by the scenario manager.

There may be times when the easiest way to create a new scenario is to copy the

files and rename the scenario number and then change files as necessary including

the output files (*6.ARR) within the text editor or the scenario manager.

SCENARIO MANAGER

USERS G

UID

E

USERS GUIDE


Getting the DataThe Mason Street model add-in, which allows the user to run the Mason Street

model within a dialog box in TransCAD, is included as a zip file named

MasonModelxxyyzz.zip. This file contains the necessary components to run the

Mason Street model from the dialog box in TransCAD.

There is a performance module add-in zip file named Performancexxyyzz.zip. This

file contains the ui database needed to run the performance module.

The Mason Street model scenarios will be given to you in three zipped files for each

year. The data zip file contains all input data needed for the specified model, includ-

ing the transit networks. The net zip file contains the networks for the model. The

transit zip file contains the transit route system needed for modifying and creating

new transit networks. The file names are interpreted as follow:

WWdataxxyyzz.zip, WWnetxxyyzz.zip, and WWtransitxxyyzz.zip

where:WW= the model year (1998 = 98, 2020 = 20, etc.)xx = the month the data was completed or zippedyy = the day the data was completed or zippedzz = the year the data was completed or zipped

Loading the Mason Street Model Add-In1.Extract the files from the MasonModelxxyyzz.zip file using a standard com-

pression software (WinZIP) into a folder in your TransCAD directory (perhapsC:\TransCAD\Mason). The files will be:

• MasonPhase3.rsc

• Mason.bmp

• MSP3_UI database (including all necessary components)

2.In TransCAD, follow these commands. You will only have to do this once.

• Tools...

• Add-Ins...

• Setup...

• Add

• Type = Dialog Box

• Description = Mason Street Phase 3 Planning Model (This is what you willsee in the Add-ins box)

• Name = Mason Street Model (This needs to be exact)

• UI Database = the path you extracted the ui database to (perhapsC:\TransCAD\Mason\MSP3_ui)

• OK

3.To open the Mason model dialog box, highlight the Mason Street Phase 3Planning Model and click OK.

Loading the Performance Module Add-In1.Extract the files from the Performancexxyyzz.zip file using a standard compres-

sion software (WinZIP) into a folder in your TransCAD directory (perhapsC:\TransCAD\Performance). The files will be the perf_ui database (including allnecessary components).

2.In TransCAD, follow these commands. This will only need to be done once.

• Tools...

• Add-Ins...

Mason Model Users Guide


USERS GUIDE

• Setup...

• Add

• Type = Dialog Box

• Description = Mason Street Performance Module (This is what you will seein the Add-ins box)

• Name = PERFORMANCE (This needs to be exact)

• UI Database = the path you extracted the ui database to (perhapsC:\TransCAD\Performance\perf_ui)

• OK

3.To open the Performance Module dialog box, highlight the Mason StreetPerformance Module and click OK.

Loading the Alternatives1.Extract the files from 98dataxxyyzz.zip into a folder in the TransCAD directory

(perhaps C:\TransCAD\Mason\98INPUTS). This extracts the multiple input filesneeded to run the model.

2.Extract the files from 20dataxxyyzz.zip into a folder in the TransCAD directory(perhaps C:\TransCAD\Mason\2020INPUTS). This extracts the multiple inputfiles needed to run the model.

3.Tools…Geographic File…Restore the files from 98netxxyyzz.zip into a folder inthe TransCAD directory, it is suggested that these files are in a different folderthan the model inputs (perhaps C:\TransCAD\Mason\98Networks).

4.Tools…Geographic File…Restore the files from 20netxxyyzz.zip into a folder inthe TransCAD directory, it is suggested that these files are in a different folderthan the model inputs (perhaps C:\TransCAD\Mason\2020Networks).

5.Extract the files from 98transitxxyyzz.zip into a folder in the TransCADdirectory, it is suggested that these files are in a different folder than the modelinputs (perhaps C:\TransCAD\Mason\1998Transit). Make sure you let theextractor program use the same folders as it was saved in.

6.Extract the files from 20transitxxyyzz.zip into a folder in the TransCADdirectory, it is suggested that these files are in a different folder than the model

inputs (perhaps C:\TransCAD\Mason\2020Transit). Make sure you let theextractor program use the same folders as it was saved in.

NOTE: The following folder definitions have been found to be very useful, but the

files can be set up based on user preferences.

C:\TransCAD\Mason\98InputsC:\TransCAD\Mason\2020InputsC:\TransCAD\Mason\1998TransitC:\TransCAD\Mason\2020TransitC:\TransCAD\Mason\98networksC:\TransCAD\Mason\2020NetworksC:\TransCAD\Mason\alternative1C:\TransCAD\Mason\alternative2C:\TransCAD\Performance

USERS GUIDE


Setting Up the Scenarios

1.Open the Mason Street model dialog box

2.Click on Setup Scenarios

3.Add and Delete scenarios as needed. You cannot delete the first scenario! (ClickOK twice to respond to these questions.)

4.To edit a scenario

a. Check the Edit Scenario box

b.Enter a description that you will see from the main dialog box

c. For each tab

i) Click on each file name button and enter the correct file name. An openfile dialog box will appear for you to highlight and find the correct filesor to type in the output file names.

Running a Scenario

1.Choose the desired scenario from the scenario pull-down list in the MasonStreet model dialog box.

2.If you want to see the results after each step is completed, make sure “Stopafter each step” is checked (default). If you want to run the model frombeginning to end, unclick that box.

3.Create Networks if there have been any changes. It will not hurt anything if youcreate a new network and you haven’t changed anything.

4.Create Networks to Run Trip Assignment are completely automated and do notrequire any user input when the model does not stop after each step.

5.Run Speed Feedback replaces the input congested and freeflow speeds in thegeographic (*.dbd) file with the speeds that resulted from the previous tripassignment.

6.Quit when you are finished running the model.


USERS GUIDE

Running the Performance ModuleThe performance module creates a text file that contains the file inputs and outputs

for the scenario along with multiple summaries for analyzing the model run.

1.Open the performance module add in.

2.The scenarios will be the most recent scenarios from the Mason Street modelscenario manager

3.Enter a title that will be displayed at the top of each page in the summary

4.Check any available item to create that report

5.Click OK

6.A file (the summary file) will be created and can be opened in any text editor toview and print

7.The performance module does take some time to complete all of the summa-ries. Please be patient – TransCAD will display a note when it is finished.

Creating Transit Network FilesTransit network files (*.tnw) are the only transit files needed to run the model. The

model does not look at the route system. The following is a summary of options

when creating transit network files.

1.Create a selection set of the routes to be included in the transit network.

2.Create a selection set of non transit links to be included.

a.Walk access only networks should include links where Facility_No = 12

b.Drive access networks should include links where Facility_No = 10 or 11,and walk links (Facility_No = 12) within a mile of the routes most used bydrive access trips. (This allows greater egress options for drive accesstransit riders.)

3. Create a selection set of non transit centoids using Dataview…Select by Location

(based on the selection set created in step 2) and then removing nodes where

ZONE=null from this set using Dataview…Select by Condition.

4.The line dbd file should have the following attributes

a. In Vehicle Travel Time = IVTT = Min((Length/3)*60, (Length/(Speed-10))*60)

b.Walk Time = WalkT = (RALength/3)*60

c. Drive Access Travel Time = DACC = (RALength/Speed)*60

d.Walk access links have a speed of 3 mph

e. Drive access links have a speed of 20 mph

f. Walk access links to park and rides have a speed of 3 mph

g.Drive access links and walk access links to park-and-rides have direction-ality (in the direction TO the park and ride and the transit stop)

5.When creating a transit network, select the following options

a.Route System selection set created in step 1

b. Include IVTT in the Line Layer Fields

USERS GUIDE


c. Include all Route Fields

d. Include all Stop Fields except for “Field 1”, “Field 2”, “Field 3”, “Field 4”,and “Field 5”

e. Merge stops within 0.05 miles

f. Merge non transit links within 0.17 miles

g.Use Non Transit Links

h.Non transit link selection set created in step 2

i. Mapping:

i) For Walk access, IVTT = WalkT

ii) For Drive access, IVTT = DACC

j. Ignore link directions on walk access networks, but do not ignore linkdirections on drive access networks

6.Network Settings should include

a.General Tab

i) Minimize IVTT

ii) Pathfinder Method

iii) VOT = 0.5

iv) VImp = 0.5

v) Max Xfers = 3

vi) Use Centroids – selection set created in step 3

b.Fare Tab

i) Regular/Route = none

ii) Regular/Global = 0.00

iii) Flat Fare

c. Cost Function Tab

i) Not Applicable (should be all grayed out!)

d.Weights Tab

i) In Vehicle Time/Route = None

ii) In Vehicle Time/Global = 1.00

iii) Transfer Time/Route = None

iv) Transfer Time/Global = 1.00

v) Waiting Time/Route = None

vi) Waiting Time/Global = 2.50

vii)Non-Transit Time Weight = 4.68

viii) Interarrival Parameter = 0.50

e. Others Tab

i) Headway/Route = Headway

ii) Headway/Global = 15.00

iii) Transfer Time/Global = 2.00

iv) Max Wait = 60.0

Modifying the Roadway NetworkWhen modifying the roadway network, use the standard TransCAD map editing

tools to add, delete, split, and join links. Table 57 is a list of fields that need to be

filled in on the dataview for the network, the other fields are calculated during

Create Networks.

The only time node fields need to be modified is when creating a new centroid.

When creating a new centroid, fill in the appropriate values in the node dataview.


USERS GUIDE

Field

AB_Lanes

BA_Lanes

Urban Rural CBDFreeway 78 78Expressway 40 65Major Arterial 34 57 26Minor Arterial 30 48 17Collector 25 35 15Zone Connector 16 25 16Freeway Ramp 30 30Transit 20 20 20Transit Only 40 40 40Walk Access PNR 3 3 3Drive Access 20 20 20Walk Access 3 3 3

FREESPEED



BA_CAPACITY Capacity per hour per lane in the negative topology direction. Use the same values as given for AB_CAPACITY.

ALPHA The alpha parameter in the BPR function used in traffic assignment.

Freeways = 0.6All others = 0.5

The input freeflow speed in miles per hour, from the speed feedback process or the same as INPUT_SPEED.

CONGSPEED The input congested speed in miles per hour, from the speed feedback process or the table below.

AB_CAPACITY Capacity per hour per lane in the positive topology direction.

The number of lanes in the positive topology direction.

The number of lanes in the negative topology direction.

INPUT_SPEED The input freeflow speed in miles per hour.

Description and Variable DefinitionDir The directionality of the link.

0 = 2 way1 = same direction as topology-1 = direction opposite topology

DAILY_VEH_COUNTYEARTRUCK_FACTGROWTH_FACT

DISTRICT

8 = Berthoud9 = Other10 = Mason Street Corridor

The district number used in reporting processes.

REPORTS The number used in the current performance module to summarize the jurisdictions.

1 = Fort Collins Urban2 = Fort Collins Rural3 = Loveland Urban4 = Loveland Rural5 = Greeley Urban6 = Greeley Rural7 = Longmont

ADJ_COUNT The adjusted count to use to compare model results.

ADJ_COUNT = (DAILY_VEH_COUNT / TRUCK_FACT) * GROWTH_FACT

FC_NONATTAIN_AREA Used to determine the links within the Fort Collins Nonattainment Area for air quality purposes.

1 = included in the nonattainment area

0 = not included in the nonattainment area

The raw vehicle count for a link.The year the raw vehicle count was taken.The truck factor to adjust the counts.The growth factor used to adjust the counts to the model year.

10 = Walk Access PNR11 = Drive Access12 = Walk Access

FACILITY_TYPE The facility type used to determine parameters.

Freeway, Expressway, Major Arterial, Minor Arterial, Collector, Zone Connector, Freeway Ramp, Transit, Transit Only, Walk Access PNR, Drive Access, or Walk Access

6 = Zone Connector7 = Freeway Ramp8 = Transit9 = Transit Only

AREA_TYPE The area type used to determine parameters.

Urban, Rural, or CBDFACILITY_NO The facility number used to determine

parameters.

1 = Freeway2 = Expressway3 = Major Arterial4 = Minor Arterial5 = Collector

BETA The beta parameter in the BPR function used in traffic assignment.

All links = 4.0AREA_NO The area number used to determine

parameters.

1 = Urban2 = Rural3 = CBD

BETA2 The beta parameter used in the BPR function to calculate post assignment travel time.

Urban Freeways = 4.0Other Urban Links = 4.0Rural Freeways = 4.0Other Rural Links = 6.0CBD Links = 2.4

ALPHA2 The alpha parameter used in the BPR function to calculate post assignment travel time.

Urban Freeways = 0.60Other Urban Links = 1.50Rural Freeways = 0.45Other Rural Links = 0.32CBD Links = 1.78

Table 51: Network Fields

TRANSPORTATION CORRIDOR

Documents

Mason Street Transportation Corridor Master Plan