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a test of transferability: the sE florida activity-based model

14 th TRB National Planning Applications Conference May 5-9, 2013, Columbus, Ohio . a test of transferability: the sE florida activity-based model. Rosella Picado Parsons Brinckerhoff. Background.

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a test of transferability: the sE florida activity-based model

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  1. 14th TRB National Planning Applications Conference May 5-9, 2013, Columbus, Ohio a test of transferability:the sEflorida activity-based model Rosella Picado Parsons Brinckerhoff

  2. Background • Southeast Florida is home to 5.5 million people, spanning Miami-Dade, Broward and Palm Beach counties • Relatively disperse travel patterns with significant congestion on Turnpike and north-south freeways • Growing interest in improving transit, expand toll and managed lane infrastructure, mitigate adverse EJ impacts • Trip-based model reaching its limits, especially regarding variably-priced tolling, fare policies, spatial detail, EJ analysis

  3. SE Florida ABM • Coordinated Travel – Regional Activity-based Modeling Platform Family of ABMs • Main features: • Explicit intra-household interactions • Continuous temporal dimension (half-hour time periods) • Fine spatial dimension (12,000 MAZs) • Faithful transit access coding • Distributed values of time • Integration of location, time-of-day, and mode choice models

  4. Model Development Strategy • Transfer the San Diego ABM • Adopt CT-RAMP structure and sub-models • Adopt model parameters estimated with San Diego household survey data • Update certain models to reflect SE Florida conditions: • input data availability (employment, population controls) • modal supply • trip assignment methods • ancillary models • Calibrate models to SE Florida travel patterns • Re-specify models that fail to perform well

  5. Why Model Transfer? • Schedule: • To use the ABM in the development of the 2013 Long Range Transportation Plan • Approximately 18 months available for model development was insufficient time to estimate & validate all models • Data: • Quantity and quality of NHTS SE Florida sample may preclude statistically significant estimation of some models and/or population effects • Largely sufficient for calibration, with caveats

  6. Data Limitations • Small sample size – 2,000 households • Some subareas within model region under-represented • Retired households over-sampled • College students and children under-represented • Missing data, ‘ungeocodable’ activity locations, etc. • Incomplete transit on-board survey

  7. Assessing the Model Transfer Outcome • Evaluate initial estimated travel patterns against model calibration targets • Regional targets for important person markets • Sub-regional where data allow • Assess the magnitude of constant or parameter adjustments to match targets • Importance of model calibration targets • Based on NHTS and supplemented with other sources • Evaluated for reasonableness • Compared to targets from other regions

  8. Work Location Model - initial results Tour Frequency (%)

  9. Work Location Model - calibrated Tour Frequency (%)

  10. School Location Model - initial Tour Frequency (%)

  11. School Location Model - calibrated Tour Frequency (%)

  12. Eating Out Location Model - initial Tour Frequency (%)

  13. Daily Activity Pattern Model

  14. Daily Activity Pattern Model

  15. Non-Mandatory Tour Frequency Calibrated Estimated Tour Frequency (%) Initial

  16. Non-Mandatory Tour Frequency Estimated Tour Frequency (%) Calibrated Initial

  17. Work Departure and Arrival Times Initial

  18. Shop Tour Departure Time Initial

  19. Shop Tour Departure Time Calibrated

  20. Work Tour Mode Choice

  21. Work Ahead • Finalize model calibration • Validation to traffic counts and transit boardings • Future year forecast and sensitivity tests

  22. Conclusions / Lessons Learned • SANDAG CT-RAMP ABM is able to reproduce most regional travel patterns in SE Florida • Largest differences between observed and initial model forecasts: • non-mandatory tour location • CDAP and tour frequency for college students, part-time workers, pre-school children • Modest constant adjustments sufficient to calibrate the model

  23. Conclusions / Lessons Learned • Supplemental data sources important to validate calibration targets and selected model outputs • Unable to observe transferability at high levels of disaggregation

  24. Acknowledgments • Shi-Chiang Li, Florida DOT • Paul Larsen, Palm Beach MPO • Paul Flavien, Broward MPO • Larry Foutz, HNTB (formerly Miami-Dade MPO) • Ken Kaltenbach, The Corradino Group • Sung-Ryong Han, BCC Engineering • Bill Davidson, Ben Stabler, Jinghua Xu

  25. Questions? Rosella Picado Parsons Brinckerhoff Seattle, WA picado@pbworld.com | (206) 382-5227

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