Microsimulation of IntraUrban Commercial Vehicle and Person Movements

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• Microsimulation of Intra-Urban Commercial Vehicle
  and Person Movements

Ofir Cohen, PB, San Francisco John Gliebe,
Portland State University Doug Hunt, University
of Calgary
11th National Transportation Planning
Applications ConferenceSession 11 May 8, 2007,
Daytona Beach, Florida
Contact Information 415-243-4645
coheno_at_pbworld.com
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Agenda

• Motivation- why?
• Disaggregate COmmercial Model Scope
• Survey and Segmentation of Establishments
• Model Components
• Calibration results

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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 dont represent service provision,
  sales calls and travel for meetings
• Large variation in firm operations
• Practical yet realistic approach needed

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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, drivers lunch, etc.

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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

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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

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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

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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)

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Traveler Worker
TAZ 1457 17 Construction Workers
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D.C Log Sum
Industrial Establishments
Service Establishments
Time coef-0.1677
Time coef-0.2198
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Vehicle Type Model

• Assign to each traveling employee a vehicle type
  for the entire day

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Vehicle Use by Establishment Type
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Start Time Model
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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

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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 0730-2130
• 4. Other- Personal needs (Food, Gas)
• Available only between 0600-2245
• 5. Back To Establishment - ends this tour
• 6. Stay in Current Activity - increment times
• by 5 minutes, simulates the trip duration

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0805 AM
0620 AM
0652 AM
0657 AM
0720 AM
0920 AM
0800 AM
1200 AM
T.P
T.P
T.P
T.P
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Trip Purpose Model
U (purpose) c1c2EstablishmentType
c3currentPurpose StayEffectConstant
timeWindowConstanttime c4tourDuration
c5DayDuration c6stayDuration c7ln
(stayDuration) c8VehicleType
SERVICE TRIP
GOODS TRIP
OTHER TRIP
MEETING TRIP
RETURN
STAY
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Next Stop Location
U(TAZ)f( Chosen Purpose, Establishment, Vehicle,
eTime, tTime, Jobs(14 categories), HH, LU type)
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Next Stop Location Results
Industrial Establishment Destinations
Wholesale Establishment Destinations
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Establishment Destination
Columbus Area
Destination locations
Establishment locations
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Destination Choice Distance Calibration
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Lesson learned

• Worth the effort shouldnt be neglected.
• Capture real-time decisions
• Huge variation in patterns
• Estimation shouldnt be over-segmented.
• More vehicle types.
• Can be applied for Weekend HH activity model
• Easily calibrated

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Acknowledgements

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

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Questions?