L4 Trip Generation

Transport Planning: Trip Generation

Travel-Demand Forecasting Process

Trip Generation

Trip Distribution

Mode Split

Transportation

Network & Service

Attributes

Link & O-D Flows,

Times, Costs, etc.

Trip Assignment

Population & Employment Forecasts

General Framework of 4-Step Models

How many trips

will be made?

Trip Generation

Trip Distribution

Mode Split

Trip Assignment

I

Oi

J

Dj

Trip Generation

I J

Trip Distribution

Tij

I J Mode Split

Tij,auto

Tij,transit

I

J

Trip Assignment

-- path of flow Tij,auto

through the auto

network

General Framework of 4-Step Models

Travel-Demand Forecasting Process

Demand for added capacity and parking facilities is not uniformly distributed throughout urban areas

Dependent on type of land use in each zone

residential

commercial

Industrial, etc.

Dependent on intensity of land use in each zone

residential density

workers per acre

shopping floor space, etc.

Travel-Demand Forecasts

Trip generation models were postulated, calibrated and validated to

relate trip-producing capability of residential areas and trip-attracting

potential of various non-residential types of land-use.

Components of Mathematical Models

Map is defined a priori

Zone boundaries defined

Based on survey data

Zone land use quantified

1st: Define Network

Map is defined a priori

Zone boundaries defined

Based on survey data

Zone land use quantified

Generate Number of Trips:

TO each zone (Attractions)

FROM each zone (Productions)

Function of Land Use and socio-demographics in each zone

2nd: Generate Travel Demands

Trip generation is a function of

land use activity

Industry Hospitals Shopping centers

Residential zones Schools ..

Workforce

Measures of land use activity

Activity Measure

Employment centre Number of jobs

Residential area

Education centre

Hospital

Retail centre

Industrial estate

Farm

By Purpose

Travel to work

Travel to school of college

Shopping trips

Social and recreational trips

Escort trips

Other trips

By Time of day

AM Peak

PM Peak

Off Peak

Aggregation Level

Person level trips

*Household level trips

*Zone level trips

Characteristics of Trips

Trip generation is performed before distribution and mode split, Therefore, in trip generation we cant use travel times, costs

These depend on knowing both origin and destination of the trip)

The total number of trips generated by a zone is assumed to be only a

function of:

Zonal attributes (population, employment, etc.)

Attributes of persons and activities in the zones (income, auto ownership, etc.).

Explanatory Variables

Home (production) end variables: population (by age, gender, etc.) number of workers (by occupation) household size auto ownership income distance from CBD .

Non-home (attraction) end variables: employment (retail, office, industrial, etc.) floor space (retail, office, industrial, etc.) .

Explanatory Variables

Various operational approaches to trip generation modelling:

Growth Rate Models

1. Trip rate models: Trips classified

2. Cross-classification (category analysis) models: Trip-makers classified

Regression models

1. Zonal

2. Household-based

3. Person-based

Trip Generation Modeling Approaches

Growth Factor Models

Simplistic method

T = G * t

Future number of trips is a function of:

Change in population,

Change in income,

Change in car ownership,

etc.

Future # of trips

Growth Factor

Current #of trips

Growth Factor (example)

Consider a zone i with 500 households

250 households (HHs) own cars

250 HHs do not own cars

Now, assume all HHs in zone i have a car in the future. How many trips will be produced?

If we assume all HHs will have a car in future what is the growth factor?:

Gi = projected car ownership/current car ownership

= 1 / 0.5 = 2

What is the projected number of trips produced by zone i?

Recall t = 2125 trips/day

Ti = Gi * ti

= 2 * 2125 = 4250 trips/day

Rates are typically associated with important generators within the region (land use)

Examples: Retail, services, manufacturing

Rates often in person-trips per thousand sq ft of land use

Rather than vehicle trips

NOTE: Planners must be careful to apply trip rate models in same context in which they were calibrated

Trip Rate Models

Trip Rate Model Example

Estimate the number of trips that will be generated by a new development with the following land-use characteristics:

Trip Rate Model Example

Make sure units

match up!

New trips

generated

Cross-classification

Also known as category analysis...similar to trip rate model

Classify households (or persons) by one or more variables

(e.g., household size AND # of cars).

Specific combinations of variables define household groups.

Assume that trip rates are relatively constant within each group.

Compute average trip rates for each group.

Zonal trips = sum of trips generated by all groups found in the zone

Provides highly detailed results

Potential Issues:

Requires large data sets

Lacks statistical goodness of fit measures

Does not require linearity (improvement)

Simple Cross-Classification Example

Household Location Vehicles Available

per Household

Persons per Household

1 2,3 4 5

Urban 0 0.57 2.07 4.57 6.95

1 1.45 3.02 5.52 7.9

2+ 1.82 3.39 5.89 8.27

Suburban 0 0.97 2.54 5.04 7.42

1 1.92 3.49 5.99 8.37

2+ 2.29 3.86 6.36 8.74

Rural 0 0.54 1.94 4.44 6.82

1 1.32 2.89 5.39 7.77

2+ 1.69 3.26 5.76 8.14

GIVEN: Daily Trip Rates (Trips per day) for each Household type

Average daily number of trips made by a HH (in a given zone)

in an urban location, with a single tenant owning 1 vehicle

Household Location Vehicles

Available per

Household

Persons per Household

1 2,3 4 5

Urban 0 100 200 150 20

1 300 500 210 50

2+ 150 100 60 0

Estimate the total number of trips that will be generated by the future population described:

GIVEN: Number of each household type for future population


Expected number of HH (in the zone) in an urban

location, with a single tenant owning 1 vehicle

Household Location Vehicles Available per

Household Persons per Household

1 2,3 4 5

Urban 0 57 414 685.5 139

1 435 1510 1159.2 395

2+ 273 339 353.4 0


COMPUTE: Future Trips Generated

To estimate total trips, sum the total trips for each household type:

Total Trips = 57+414+685.5+139+435+1510+1159.2+395+273+339+353.4+0

Total Trips generate by the zone = 5760.1 trips

= 1.45 trips/day * 300 HHs

Regression Development of an equation to predict the number of trips (per person, HH, zone) based on:

Population

Households

Car ownership

Accessibility

Number of dwellings

Employment

Etc.

The equation should relate our observed inputs and output

Objective is to estimate best fit linear relationships between dependent variable (#of trips) and one or more explanatory variables

The equation is calibrated to minimize errors

Model can be developed at the zonal or more disaggregate levels

Example: Two-variable model at the HH level:

Example: Two-variable model at zonal level:

Example: Multi-variable model at the zonal level:

Examples of Regression Models

Daily trip productions per household, all purposes 1.229 1.379 (#of vehicles per household)

Daily work trip attractions for a given zone 61.4 0.93 (Total zonal employment)

Work trip productions per zone

0.135 (Zonal population)

0.145 (Number of dwelling units per zone) -

0.253 (Total number of automobiles owned in the zone)

Regression Example (1 variable)

Y: Number of Daily Trips X: Household Size

8 3

13 7

6 3

7 2

7 3

6 2

7 3

8 4

5 2

11 5

9 4

5 2

9 5

11 6

6 2

9 4

o

o o

o o

o

o o

o o

o

x

Y

a

b

xi

Yi Observed data

{xi,yi}, i=1,,n

What values of a & b best

fit the observed data?

Y = a + bx

E, error, residual

Parameter Value Estimation

# d

ail

y t

rip

s

HH size

Regression Example (1 variable)

Y: Number of Daily Trips X: Household Size

8 3

13 7

6 3

7 2

7 3

6 2

7 3

8 4

5 2

11 5

9 4

5 2

9 5

11 6

6 2

9 4

Calibrated least square error line:

Y = 2.93 + 1.41 X

Number of Daily Trips

(dependent variable)

Household Size

(independent variable)

o

o o

o o

o

o o

o o

o

x

Y

a=2.93

b=1.41

xi

Yi

Y = a + bx

What is the model estimation at xi?

Yi =a + bxi ei



Parameter estimation for all models (linear or otherwise) involves:

Theoretically specifying the model functional form and its explanatory variables

Observing a representative sample of the systems behaviour

Defining the criterion defining best fit of the hypothesized model to the observed data

Developing a statistical valid, computationally efficient procedure for finding the best fit parameters for this problem

Evaluating the statistical performance of the estimated model and its goodness-of-fit

Regression Example (2 variables)

Number of Daily Trips Household Size Number of Vehicles 8 3 2

13 7 3

6 3 1

7 2 0 7 3 3 6 2 2

7 3 2 8 4 3

5 2 1 11 5 3 9 4 3

5 2 1 9 5 3

11 6 3 6 2 2

9 4 3

Calibrated least square error line: Y = 2.91 + 1.39 X1 + .03 X2

Additional Forms Linear:

T = 4.33 + 3.89 L1 0.005 L2 0.128 L3 0.012 L4 where

L1 = Vehicle ownership

L2 = Population density

L3 = Distance from CBD

L4 = Family income

(Source : Mertz and Hammer (1957) of BPR)

Exponential:

To = K1 Lo e -

1 t

o

Td = K2 Ld e -

2 t

d

(Source: Gupta and Hutchinson (1979))

Additional Forms

Multiplicative:

T = Po Pd Yo Yd Mo Md No t c fb .

P Population

Y Median Income

M Institutional character

N Transport supply

t Travel time

C Transport cost

F Departure frequency

(Source : Boston Washington corridor project)

Regression models are easy to construct and use.

BUT underlying assumptions, however, may be wrong:

1. Linearity.

2. No interaction between explanatory variables

3. best fit equations may give counterintuitive results

Things to keep in mind

Questions?

Documents

L4 Trip Generation