Microsimulation of Intra-Urban Commercial Vehicle and Person Movements

11th National Transportation Planning Applications Conference

Session 11: May 8, 2007, Daytona Beach, Florida* Contact Information: 415-243-4645 | coheno@pbworld.com

Ofir Cohen, PB, San Francisco*John Gliebe, Portland State University

Doug Hunt, University of Calgary

Agenda

Motivation- why?

Disaggregate COmmercial Model Scope Survey and Segmentation of

Establishments Model Components Calibration results

Motivation

Commercial travel comprises a large share of weekday urban traffic, but has received scant attention from modelers (Regan and Garrido, 2002)– ~11% of overall vehicle trips in the state. – Emphasize on Tour rather than trip– Standard freight models miss short-hauls and multi-

stop deliveries within urban areas– Freight models don’t represent service provision,

sales calls and travel for meetings– Large variation in firm operations

Practical yet realistic approach needed

Scope: What is a Commercial Trip? Intra-urban trips only – up to 50 miles*

– ACOM is an econometric model that simulates Inter-urban trips.

Weekday simulation of a typical 24 hrs* All trip purposes combinations are available Includes goods pickup and delivery, meetings,

business supply acquisition, service provision, sales, driver’s lunch, etc.

Establishment Types Industrial: 4 sub establishments categories

» Agriculture » Construction » Heavy Industry» Other ( Mines, Metal, Light Industrial, etc.)

Wholesale: warehousing and distribution Retail: stores and restaurants Transport: for-hire trucking and delivery Service: 5 sub-establishments types

» Hotel» Health» Government» Education» Other – e.g., banking, consulting

Ohio Establishment Survey

Surveys:– Data on the firm: employees, number who travel for

job, commodities, occupations– One-day activity/travel diaries – Shipment data corresponding to travel diary

Sample:– 561 public and private establishments– 1,640 workers who traveled– 1,951 work-based tours– 9,588 activity/trip records

Ohio Establishment Survey, Cont.

Limitations / Simplifications– No data on intra-establishment relationships– One vehicle per day per employee– Occupations of individuals not identified– No observations for Non-Motorized or Transit

trips– No data on delivery company such as FedEx,

DHL, or UPS

Zonal Land Use Data

Worker Traveler Generation

Vehicle Assignment

Starting Time Assignment

Next Stop Purpose Choice

Model

Next Stop Location

Choice Model

Dynamic Activity Pattern Generation

Commercial Vehicle Trip List

Zonal Land Use Data

Worker Traveler Generation

Vehicle Assignment

Starting Time Assignment

Next Stop Purpose Choice

Model

Next Stop Location

Choice Model

Dynamic Activity Pattern Generation

Commercial Vehicle Trip List

Traveler Generation Model Number of employees segmented by

establishment type is defined per TAZ Binary Logit function- an employee did a

Commercial Tour or not A traveler will do at least 2 trips (First trip+ return

to his establishment)

Establishment Industrial Wholesale Retail Transport Services All

Total Employees 2,057,520 386,460 1,471,444 264,866 4,121,853 8,302,143

% Who Travel 9% 15% 7% 14% 9% 9%

Total Travelers 180,570 56,810 103,001 38,141 379,228 757,749

Traveler Worker

TAZ 1457 = 17 Construction Workers

D.C Log Sum Industrial Establishments Service Establishments

Time coef=-0.1677 Time coef=-0.2198

Vehicle Type ModelAssign to each traveling employee a vehicle type for the entire day

Medium Heavy

Industrial -0.4678 -1.49915

Wholesale -0.36328 -0.25927

Retail -1.18581 -2.11505

Transport 1.48201 2.69641

Service -2.44889 -3.09721

Resid_LU 0.87947 0

Ind_Mix_LU 0.30882 0

Indus_LU 0 -0.48892

Office_LU -0.73201 -1.38641

Retail_LU -0.96729 0

CBD__LU -0.7541 -1.2481

Rural_LU 1.65637 2.24699

Vehicle Use by Establishment Type

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percentage

industrial wholesale retail transport service

Sector

Vehicle Distribution

Heavy Vehicle

Medium Vehicle

Light Vehicle

Start Time ModelFirst Trip of Day Starting Time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5 70 135

200

265

330

395

460

525

590

655

720

785

850

915

98010

4511

1011

7512

4013

0513

7014

35

Minutes Past Midnight

Cu

mu

lati

ve D

istr

ibu

tio

n

Wholesale Light

Wholesale Med/Hvy

Service

Day patterns formed through dynamic choice approach

Not a pattern based model Any number of tours and trips is possible Sensitive to accumulated time at multiple

levels: - activity, tour and workday duration

Previous decisions affect future decisions

Trip Purpose Model Multinomial Logit function with 6 alternatives:

1. Good - Distribution/pickup of goods2. Service - Providing Service3. Meeting - Limited to Light / Medium vehicle

– Available only between 07:30-21:30

4. Other- Personal needs (Food, Gas)– Available only between 06:00-22:45

5. Back To Establishment - ends this tour6. Stay in Current Activity - increment times

by 5 minutes, simulates the trip duration

08:05 AM

T.P

T.P

T.P

06:20 AM06:52 AM06:57 AM07:20 AM09:20 AM08:00 AM T.P12:00 AM

Trip Purpose Model

SERVICE TRIP

Establishment=Wholesale  Good Service Other Meeting Return

current- Good 1.271 -2.180 -1.314 -4.861 -0.748

current- Service -2.531 1.239 -1.982 -4.732 -1.261

current- Other 0.583 -0.155 -0.345 -3.218 -0.588

current- Meeting -2.276 -1.764 -1.322 -1.542 -0.841

current- Back to Estab -2.689 -2.645 -4.501 -4.769 0.000

Constant 0.865 -0.365 -1.303 1.333 0.000

Time Hour 08:00-09:00 1.161 1.381 1.249 2.28 -0.169

Time Hour 17:00-18:00 0.978 0.295 1.440 -0.431 0.102

Stay Duration when current= -0.041 -0.025 -0.035 -0.016 0.178

LN (Stay Duration) when current= 1.894 1.669 1.847 1.456 -0.773

Wholesale Stay Effect when current= 0.768 0.0 0.0 0.894 0.0

Overall Tour Duration -1.1E-3 4.5E-3

Total Activity Duration - current tour -0.017 0.01

Vehicle Light 0.743 1.048 0.000 0.000 0.000

GOODS TRIP OTHER TRIP MEETING TRIP RETURNSTAY

U (purpose) = c1+c2*EstablishmentType +c3*currentPurpose + StayEffectConstant + timeWindowConstant*time+ c4*tourDuration+ c5*DayDuration+ c6*stayDuration+ c7*ln (stayDuration) +c8*VehicleType

Next Stop LocationU(TAZ)=f( Chosen Purpose, Establishment, Vehicle,

eTime, tTime, Jobs(14 categories), HH, LU type)

Alt 1

Origin

Current

Alt 2

Alt 3

eTime

eTime (Time to Estab)

eTime (Time to Estab)

Next Stop Location ResultsIndustrial Establishment Destinations Wholesale Establishment Destinations

Establishment & Destination

Establishment locations Destination locations

Columbus Area

Destination Choice Distance Calibration

Goods Purpose

0.00

0.05

0.10

0.15

0.20

0.25

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Miles

Goods Purpose Target Share

Goods Purpose Results Share

Service Purpose

0.00

0.05

0.10

0.15

0.20

0.25

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Miles

Service Purpose Target Share

Service Purpose Results Share

Vehicle Type Purpose

Target Average

Modeled Average

Difference (miles)

Goods 5.658 5.740 -0.082

Service 6.284 6.354 -0.070

Other 4.694 4.786 -0.092

Meeting 6.669 6.823 -0.154

Return 9.134 9.359 -0.225

Goods 7.185 7.214 -0.029Service 6.394 6.401 -0.007Other 5.868 5.958 -0.090Meeting 8.244 8.905 -0.661Return 9.533 9.494 0.039

Goods 10.275 10.130 0.146Service 6.148 6.325 -0.177Other 5.916 6.076 -0.160Meeting 0.000 0.000 0.000Return 7.834 7.795 0.039

Light

Med

Heavy

Average Distance in Miles

Industrial Establishment Time Window Calibration

0.00

0.05

0.10

0.15

0.20

0.25

0.30

00-03 AM

03-06 AM

06-07 AM

07-08 AM

08-09 AM

09-10 AM

10-11 AM

11-12 PM

12-13 PM

13-14 PM

14-15 PM

15-16 PM

16-17 PM

17-18 PM

18-19 PM

19-20 PM

20-22 PM

22-24 PM

Trip Starting Time

Fre

qu

ency

Target Ind-Goods

Target Ind-Service

Target Ind-Other

Target Ind-Meeting

Target Ind-Back

Output Ind-Goods

Output Ind-Service

Output Ind-Other

Output Ind-Meeting

Output Ind-Back

Service Establishment Time Window Calibration

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

00-03 AM

03-06 AM

06-07 AM

07-08 AM

08-09 AM

09-10 AM

10-11 AM

11-12 PM

12-13 PM

13-14 PM

14-15 PM

15-16 PM

16-17 PM

17-18 PM

18-19 PM

19-20 PM

20-22 PM

22-24 PM

Trip Starting Times

Fre

qu

ency

Target-Good

Target-Service

Target-Other

Target-Meeting

Target-Back

Target All

Output-Good

Output-Service

Output-Other

Output-Meeting

Output-Back

Output All

Lesson learned

“Worth the effort” – shouldn’t be neglected. Capture “real-time” decisions

Huge variation in patterns

Estimation shouldn’t be over-segmented. More vehicle types. Can be applied for Weekend HH activity

model Easily calibrated

Acknowledgements

Ohio Department of Transportation– Greg Giaimo– Rebekah Anderson– Sam Granato

Questions?