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The Ohio Statewide Short Distance Travel Models

Session 17: Statewide Models: When Modeling a City Isn’t Enough. The Ohio Statewide Short Distance Travel Models. 11th National Transportation Planning Applications Conference May 6-10, 2007, Daytona Beach, Florida. The Ohio Statewide Model. Short Distance Travel Model.

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The Ohio Statewide Short Distance Travel Models

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  1. Session 17: Statewide Models: When Modeling a City Isn’t Enough The Ohio StatewideShort Distance Travel Models 11th National Transportation Planning Applications ConferenceMay 6-10, 2007, Daytona Beach, Florida

  2. The Ohio Statewide Model

  3. Short Distance Travel Model • Forecasts the person movements arising from household production and consumption of economic activities and labor. • Entirely based on probabilistic models. • Fully micro-simulated – the models are applied to each individual in the population. • Forecast daily activity patterns, tours and trips: • Residents only • Travel within 50 miles of home – except Work • Exclude business travel

  4. Short Distance Travel Model Flow

  5. Selects an activity pattern for each person in the population. Activity patterns are sequences of activities. Activity patterns consist of tours: Home-based Work-based Observed in the home interview surveys Daily Activity Pattern Model

  6. Activity Pattern Model - Segmentation

  7. Activity Pattern Model – Tour Composition Rules work tour shop tour rec. tour

  8. Daily Activity Pattern Model - Simplifications • Tours always start and end at home, or start and end at work

  9. Activity Pattern Model - Simplifications • Home-Based Tours consist of one primary destination and at most one intermediate stop per half-tour. 90% fit the simplified pattern exactly.

  10. Activity Pattern Model - Simplifications • Work-Based Tours have no intermediate stops. 85% fit the simplified pattern exactly.

  11. Daily Activity Pattern Model - Structure

  12. Choices are generalized patterns: Ignore purpose of intermediate stops if pattern has 2+ tours Ignore presence of intermediate stops if pattern has 3+ tours Submodels select purpose and number of stops if pattern was generalized Daily Activity Pattern Model - Structure END RESULT: PATTERN DISTRIBUTION IN FORECAST POPULATION SAME AS OBSERVED IN SURVEYS

  13. Daily Activity Pattern Model - Structure • Explanatory variables: • Activity-related: • Number and purpose of activities • Sequence of tours and/or activities • Number and purpose of tours • Number, purpose, presence/absence of intermediate stops • Traveler-related: • Age and gender • Household size, number of workers, income, presence and age of children • Transport-related: • Home to work distance (worker & college student models only) • Destination choice logsum by purpose

  14. Estimation Results – Worker Day Pattern Model

  15. Estimation Results – Worker Day Pattern Model

  16. Estimation Results – Worker Day Pattern Model

  17. Estimation Results – Worker Day Pattern Model

  18. Estimation Results – Worker Day Pattern Model

  19. Daily Activity Pattern Model – Calibration Patterns by Number of Tours per Pattern

  20. Daily Activity Pattern Model - Calibration Number of Tours by Tour Purpose B – tour includes a work-based subtour

  21. Daily Activity Pattern Model - Calibration Number of Trips by Tour Purpose B – tour includes a work-based subtour

  22. Tour Scheduling Model • Selects the departure time and duration of home-based tours • Multinomial logit, segmented by tour purpose • One hour resolution • Choice set consists of (departure time, arrival time) combinations – 190 total time windows

  23. Tour Scheduling Model • Uses the tour purpose hierarchy and day-pattern sequence to determine time window (choice) availability. • Utility function consists of departure time and duration continuous shift variables, and departure time and duration constants: • Sensitive to: • Day-pattern composition effects • Traveler effects • Transport effects

  24. Estimation Results - Work Tour Scheduling Model

  25. Estimation Results - Work Tour Scheduling Model

  26. Estimation Results - Work Tour Scheduling Model

  27. Estimation Results - Work Tour Scheduling Model

  28. Estimation Results - Work Tour Scheduling Model

  29. Estimation Results - Work Tour Scheduling Model

  30. Estimation Results - Work Tour Scheduling Model

  31. Tour Scheduling Model - Calibration

  32. Tour Scheduling Model - Calibration

  33. Tour Scheduling Model - Calibration

  34. Intermediate Stop Location Model • Selects the location of intermediate stops on tours • Multinomial logit • Choice set is a function of tour mode • Segmented by tour purpose • Utility function similar to destination choice model, • But structured to minimize ‘out of direction’ travel time

  35. Estimation Results - Intermediate Stop Location Model

  36. Intermediate Stop Duration Model • Selects the duration of intermediate stops • Multinomial logit • Segmented by tour purpose • Choice set: • One hour resolution • Constrained by tour duration • Sensitive to: • Deviation distance • Stop position (inbound/outbound) • Day pattern composition • Tour schedule

  37. Estimation Results - Intermediate Stop Duration Model

  38. Estimation Results - Intermediate Stop Duration Model

  39. Calibration – Intermediate Stop Duration – All Tours Outbound Stop Inbound Stop

  40. PB OSMP TeamShort Distance Travel Models Rosella Picado Joel Freedman Andrew Stryker Greg Erhardt Ofir Cohen Christi Willison

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