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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 | [email protected]
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
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
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
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