Download Thurston Region Multimodal Travel Demand

THURSTON REGION MULTIMODAL TRAVEL
DEMAND FORECASTING MODEL
IMPLEMENTATION IN EMME/2
- Presentation at the 15th International
EMME/2 Users’ Group Conference
Oct. 18, 2000
Jin Ren, PE, Transportation Engineer
Thurston Regional Planning Council
Olympia, WA (www.trpc.org)
TRPC Technical Modeling Process
Thurston County Travel Surveys
Multimodal Network Building
Household Sub-Models
Trip Generation
Daily Trip Distribution by Purposes
Daily Mode Choices by Purposes
Time of Day Models
Multi-Class Auto Assignments
(By Time Periods)
Transit Person Trip Assignments
(By Time Periods)
Vehicle Trip Calibration
Transit Trip Calibration
Travel “Skims” Data Preparation
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EMME/2 Multimodal Network Building
Travel Time and Distance by Modes
(Walking/Biking/Auto/Transit)
Intrazonal Travel Time and Distance
Distance-Based Housing/Employment
Density by Traffic Analysis Zones (TAZ)
Travel Time-Based Transit Accessibility
Mix Use Index (Area-Based Densities)
Area-based Local Intersection Density
Household Sub-Models
(Multinomial Logit Choice Modeling)
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Household Worker (0, 1, 2, 3+)
Household K-12 Schoolchild (0, 1, 2, 3+)
Household Auto-Ownership (0, 1, 2, 3+)
Trip Generation Models
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Cross-classified Household Trip Rates:
1998/1999 Household Travel Survey
Truck Freight Trip Model:
1997 Riebee Freight Survey Data
External Trip Generation Model:
1997 I-5/SR-101 O-D Surveys and Vehicle
Classification Counts
Household Cross-Classification Schemes
for Trip Production
Trip Purpose
Classification Variables
Home-Based Work:
Home-Based
Shopping:
Home-Based School:
Home-Based College:
Home-Based Other:
Workers in the household (0, 1, 2, 3+ Workers)
Household Size (1, 2, 3, 4+ persons)
X Workers(0, 1, 2, 3+ workers)
Student (K-12) in household (0, 1, 2, 3+ students)
Number of persons in household ( 1, 2, 3, 4+ persons)
Number of persons in household (1, 2, 3, 4+ persons)
X Vehicles (0, 1, 2, 3+ vehicles)
Household size (1, 2, 3, 4+ persons)
Workers in the household (0, 1, 2, 3+ workers)
Other-Other:
Work-Other:
1998 Daily Trip Production Calibration
Trip Purpose
HB-Work
HB-Other
HB-Shopping
HB-School
HB-College
Work-Other
Other-Other
Daily Total
Average
Trips/Household
BeforeCalibration
Productions
127,785
218,925
72,238
52,394
7,197
85,970
156,510
721,019
8.99
AfterCalibration
Productions
140,564
240,818
79,462
57,633
7,917
94,567
172,161
793,122
9.89
Model Trip
Production
Distribution
17.7%
30.4%
10.0%
7.3%
1.0%
11.9%
21.6%
100.0%
Note: Trip production expansion factor is found to be 1.10.
Expanded
Survey Trip
Production %
18.0%
29.6%
10.2%
7.4%
1.1%
12.1%
21.6%
100.0%
8.66
Daily Destination Choice Model
(Multinomial Logit Models with Size Variables)
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O-D Travel Time from Auto Assignments
1998 Households, Employees by Retail,
Office, Service, Government and Other
The standard formula for utilities is:
Utilij= exp(*timeij+*timeij2+*timeij3+ln(1…k*Employmentj1...jk
+ j*Householdsj))
Where
, , ,  and  are parameters or estimated coefficients
1…k stand for different employment sectors
i represents a ‘production’ TAZ
j represents an ‘attraction’ TAZ
Daily Mode Choice Modeling
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Drive-Alone Vehicle or Person Trips
Drive-with-Passenger Vehicle or
Person Trips
Passenger-Only Person Trips
Transit Person Trips
Walk Person Trips
Bike Person Trips
Variables Impacting Mode Choices
(Multinomial Logit Choice Modeling)
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Land Use Variables (Xi): Employment Density,
Transit Accessibility, Mixed-Use, & Parking Cost
Household Variables (Yj): Household Size, AutoOwnership, Worker Size and Income Status
Network Skims Variables (Zk): Local Intersection
Density and Point-to-Point Travel Time
The standard logit utility function:
Utilij= exp( +i*Xi+j*Yj+k*Zk)
Where , , , and  are parameters or estimated coefficients
Time-of-Day Models
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Production-Attraction and Attraction-Production
Peaking Factors (Time-of-Day Factors)
1998 AM Peak Hour Trip Tables by Modes
1998 Mid-Day Hour Trip Tables by Modes
1998 PM Peak Hour Trip Tables by Modes
Add 1998 Inbound/Outbound/ Through Vehicle
Trips for AM, Mid-day and PM Hours
Trip Assignments
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1998 Multi-Class Auto Assignments
by Time Periods
1998 Transit Person Trip Multi-Path
Assignments by Time Periods
Feedback and Looping Process to
Reach Ideal Equilibrium
Model Calibration Process
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Goodness-of-Fit Statistical Testings
Control Total or Percentage Checks:
- Household Numbers
- Trip Productions
- Mode Splits
- Average Vehicle Occupancies
Screenline Analysis by 18 Screenlines
Transit Ridership Calibration to 1998
Transit Ridership Surveys
In Conclusion
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For the first time, our region is developing a
multimodal travel demand forecasting model
For the first time, we are using local survey
data to develop a regional model
Model estimation and application hand in hand
Peer review groups and documentation
Effective integration of software tools for
data preparation and analysis in house
Robust EMME/2 Matrix Manipulation and Macros