1. 16th TRB National Transportation Planning Conference May 14-18, 2017
Benefits of Automating Calibration/Validation of Oregon MPOs Travel Demand Models
I am going to talk about “Benefits of Automating ….”
As you can see from the map, except for the yellow areas of 3 MPOs, Oregon DOT takes cares of the rest of urban and statewide travel demand models.
16th TRB National Transportation Planning Conference
May 14-18, 2017
Jin Ren, P.E., Martin Mann, Sam Ayash, Tricia Tanner Oregon DOT, Transportation Planning Analysis Unit
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2. Relevant Background Information
What got to this point of automation?
The large number of models require resource efficient
Three sets of modeling structures: 5 MPOs and 15 small urban areas, and statewide
Similar deadlines for 3 MPOs’ Regional Transportation Plans
2010 Oregon statewide Household Activity Survey and local bus on-board surveys enable step-wise calibrations
2010 US Census describes the household characteristics by census block groups, tracts and PUMA
Automating calibration/validation process for three MPOs’ demand models is very challenging but approachable
What got us to automation of calibration/validation of 3 MPO models?
We have 5 MPOs and 15 Small urban area models and statewide activity-based model.
Although we have 3 sets of model structures for each type of models, we still need to be resource efficient.
We have similar deadlines for 3 MPOs Regional Transportation Plan update in 2016.
We have 2010 statewide household activity survey, bus-on-board survey, university survey and census data for validation.
The automation process is challenging for sure but we considered it approachable.
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3. Calibration Data Source
JEMnR Model Structure:
Joint Model Estimation in R
Calibration Data Source
Rogue Valley MPO: Nine local jurisdictions along I-5, 175,000 population and 72,000 employment
About 1300 household samples and bus on-board surveys
Corvallis, Albany MPOs and Lebanon: trip-based model integrated with tour-based university model, 131,000 population, 64,000 employment and 22,000 students
About 800 household samples, university survey and bus on-board surveys
Bend MPO and Redmond: Tourist towns and resorts, with 125,000 population and 52,000 employment
About 900 household samples and on-board surveys
Here is a snapshot of three MPOs for calibration automation. They share the same model structures (Joint Model Estimation in R), but have different land use characteristics, such as: university town in Corvallis and tourist town in Bend.
Fortunately we have relatively adequate household activity survey samples to validation, plus bus-board surveys and university survey.
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4. Automated Stepwise Calibration/Validation
TG Rates
Trip Distribution
Automation
Mode Choice
Peaking
3 MPO TG from OHAS
NHTS Reference
Auto Demand Adjustment
Intra-zonal Travel Skims
Average Model Travel Distances
Scaled Utilities
K-Factors for District-to-District Travel
OHAS trip records by Market Segmentation
Bus-on-Board Survey records
PB’s Approach to OHAS/On-Board Normalized Targets
OHAS Peak Factors by purpose
Peak Factor Adjusted by PM/Daily Counts
Iterative Recalibration Process in R-Scripts
From Users’ perspectives instead of developer’s perspectives, I would like to highlight the step-wise process in terms of automation.
Unified approach with alpha, beta and gamma testing of the running R-scripts, or cross-checking one another model
Automation addresses standard conditions, but flexible to alter to each MPO’s needs
Efficiency aspect: how to proceed and what proper data format to address data needs (with bus on-board surveys in RVMPO/CALM and without in BNR), urban needs (University Model in CALM) and analysis needs
Repeatability is demonstrated by the following updates: peaking factors, external models, District-to-district K-factor tests, trip generation scaling by trip purposes, multi-class demand adjustments, volume delay functions according to NCHRP716, and thin and rich bus survey data
Household and Trip Generation Control Totals by MPOs or Combined OHAS
Demand/Supply Input Revisions (VDFs, 1-Way Walk Access, two-way bus network coding)
Complex Mode Choice Coefficients by Market Segmentations
Destination Choice Scaling and Districting
Daily and Peak Screenline Analysis for Model Validation

5. Complex Mode Choice Calibration
by Trip Purpose and by Market Segmentation
7 Trip Purposes
6-7 Travel Modes
2 Market Segmentations
HB-Work
Drive Alone
HB-Other
Drive w/Passenger
3 Household Income Groups
HB-Shopping
Passenger Only
HB-Purposes
I would like to show case an example of multinomial logit mode choice model calibration:
It is especially complex because of
6-7 Mode choices by trip purposes, by market segmentations of HH income and auto sufficiency
Calibration algorithms were peer reviewed by Keith Lawton
HB-Recreation
Bus Walk-Access
Each Trip Purpose
4 Household Auto Sufficiency Groups
HB-College
Bus Auto-Access
NHB-Work
Bike
NHB-Other
Walk

6. Mode Choice Bias Constants, HBW Market Segmentation and Automation Control
Large number of mode choice bias constants to calibrate, it was even more complicated by market segmentation of utility constants calibration.
It was all implemented in one the Loop Control file, in that we can aggregate disaggregate to 3-income groups, auto sufficiency or individual trip purposes.
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7. Main Benefits of Automation
Repeatability of calibration processes
Transferability of coding, checks and balances
Rapid trial and error runs
Efficiency in numerous iterations
Exploration of data and methodologies
Ability to deal with full and thin observed data
Flexible and responsive tools in 3 MPOs
Variations for every model component
… and it’s fun!
Values or benefits of automation process:
Allows to shorten amount of time
Importance of the tool: variable and flexible
Unique and repeatable in benefiting several agencies at the same time
Common lessons for the unified modeling structures (alpha, beta, gamma tests)
Reasonability of automation approach (cross-check model reasonableness)
Allows us the ability to address things that we discover later at the game (district-to-district K factor adjustments)
Benefits in higher number of calibration iterations of variabilities and many segmentations
Robustness in testing different scenarios:
intra-zonal changes;
skims in mode choices;
market segmentations;
3 MPOs’ k-factor in trip generation and distributions; and
volume-delay function updates
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8. ACKNOWLEDGEMENTS Peer Review Panel ODOT TPAU Contributing Staff
Keith Lawton, Keith Lawton Consulting, Inc.
Brian Dunn, P.E., Transportation Planning Analysis Manager
Alex Bettinardi, P.E., Senior Integrated Analysis Engineer
Sam Ayash, Senior Transportation Analyst/Modeler
Martin Mann, Transportation Modeler/Programmer
Jin Ren, P.E., Senior Transportation Analyst/Modeler
ODOT TPAU Contributing Staff
Beth Pickman, Transportation Analyst/Modeler
Tricia Tanner, EIT, Transportation Analyst/Modeler
Joseph Meek, P.E., Transportation Analyst/Modeler
Peter Schuytema, P.E., Senior Transportation Analyst
Richard Arnold, P.E. Transportation System Analyst Engineer
Tara Weidner, P.E., Senior Integrated Analysis Engineer
Four of our presenters implemented the automation of calibration/validation of 3 MPO models, however, I would like to use this opportunity to acknowledge our Peer Review Panel and other contributing staff.
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