1. 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

2. Agenda  Motivation- why?  D isaggregate CO mmercial M odel Scope  Survey and Segmentation of Establishments  Model Components  Calibration results

3. 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

4. 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.

5. 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

6. 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

7. 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

9. 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) EstablishmentIndustrialWholesaleRetailTransportServicesAll Total Employees2,057,520386,4601,471,444264,8664,121,8538,302,143 % Who Travel9%15%7%14%9% Total Travelers180,57056,810103,00138,141379,228 757,749

10. Traveler Worker TAZ 1457 = 17 Construction Workers

11. D.C Log Sum Industrial EstablishmentsService Establishments Time coef=-0.1677Time coef=-0.2198

12. Vehicle Type Model Assign to each traveling employee a vehicle type for the entire day MediumHeavy Industrial-0.4678-1.49915 Wholesale-0.36328-0.25927 Retail-1.18581-2.11505 Transport1.482012.69641 Service-2.44889-3.09721 Resid_LU0.879470 Ind_Mix_LU0.308820 Indus_LU0-0.48892 Office_LU-0.73201-1.38641 Retail_LU-0.967290 CBD__LU-0.7541-1.2481 Rural_LU1.656372.24699

13. Vehicle Use by Establishment Type

14. Start Time Model

15. 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

16. Trip Purpose Model  Multinomial Logit function with 6 alternatives: 1. Good - Distribution/pickup of goods 2. Service - Providing Service 3. 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 tour 6. Stay in Current Activity - increment times by 5 minutes, simulates the trip duration

17. 08:05 AM T.P 06:20 AM06:52 AM06:57 AM07:20 AM09:20 AM08:00 AM T.P 12:00 AM

18. Trip Purpose Model SERVICE TRIP Establishment=Wholesale GoodServiceOtherMeetingReturn current- Good1.271-2.180-1.314-4.861-0.748 current- Service-2.5311.239-1.982-4.732-1.261 current- Other0.583-0.155-0.345-3.218-0.588 current- Meeting-2.276-1.764-1.322-1.542-0.841 current- Back to Estab-2.689-2.645-4.501-4.7690.000 Constant0.865-0.365-1.3031.3330.000 Time Hour 08:00-09:001.1611.3811.2492.28-0.169 Time Hour 17:00-18:000.9780.2951.440-0.4310.102 Stay Duration when current= -0.041-0.025-0.035-0.0160.178 LN (Stay Duration) when current= 1.8941.6691.8471.456-0.773 Wholesale Stay Effect when current= 0.7680.0 0.8940.0 Overall Tour Duration -1.1E-34.5E-3 Total Activity Duration - current tour -0.0170.01 Vehicle Light 0.7431.0480.000 GOODS TRIPOTHER TRIPMEETING TRIPRETURNSTAY U (purpose) = c1+c2*EstablishmentType +c3*currentPurpose + StayEffectConstant + timeWindowConstant*time+ c4*tourDuration+ c5*DayDuration+ c6*stayDuration+ c7*ln (stayDuration) +c8*VehicleType

19. Next Stop Location U(TAZ)=f( Chosen Purpose, Establishment, Vehicle, eTime, tTime, Jobs(14 categories), HH, LU type)

20. Next Stop Location Results Industrial Establishment DestinationsWholesale Establishment Destinations

21. Establishment & Destination Establishment locations Destination locations Columbus Area

22. Destination Choice Distance Calibration

25. 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

26. Acknowledgements  Ohio Department of Transportation –Greg Giaimo –Rebekah Anderson –Sam Granato

27. Questions?