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 Initiative Title Goes Here

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. Initiative Title Goes Here

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. Initiative Title Goes Here

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

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

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. Initiative Title Goes Here

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 Initiative Title Goes Here

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. Initiative Title Goes Here