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Application of a Disaggregate Quasi-Dynamic Model of Park-and-Ride Lot Choice

John Gibb DKS Associates Transportation Solutions. Application of a Disaggregate Quasi-Dynamic Model of Park-and-Ride Lot Choice. The Park-and-Ride Problem for Transit Auto Access:. Which park-and-ride transit stop for a trip Getting level of service “skim” values for auto and transit legs

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Application of a Disaggregate Quasi-Dynamic Model of Park-and-Ride Lot Choice

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  1. John Gibb DKS Associates Transportation Solutions Application of a Disaggregate Quasi-Dynamic Model of Park-and-Ride Lot Choice

  2. The Park-and-Ride Problem for Transit Auto Access: • Which park-and-ride transit stop for a trip • Getting level of service “skim” values for auto and transit legs • Assigning auto and transit legs

  3. Customary Solutions(Trip-Based) • Zone-Station links by auto access “shed” • Capacity restraint by art, trial and error • Drive legs not assigned • Intermediate zone • EMME triple-index (convolution) • Multinomial logit • Capacity restraint by shadow-price

  4. Individual trip modeling- as in activity-based model • Heterogeneous choice sets & behavior • Time-specific • Sub-mode choice • Single outcome per choice • Determines auto & transit trips in both directions

  5. “Real world”: Parking available to all until full • Time-dependent choice set • Arrival time determines individual’s priority (not drive distance or analyst’s judgment) • Commuter behavior: • Know when lots fill • No frustrated arrivals to full lots

  6. Original Sacramento Application: Chronological Order • One-pass algorithm: • Sort trips by presumed departure time • Choose best-utility among available lots • Accumulate parking loads; make unavailable when full

  7. Limitations of the one-pass method • Loss of choices • Departure & parking-arrival time varies among alternatives • One can leave earlier to beat a lot’s fill-time Improved method for Sacramento update and new Seattle ABM in progress…

  8. Crawford-Knoer matching algorithm (1981) • Generalizes Gale-Shapely (1962) • Hospital-residents, college admissions, stable marriage problems • Iterative rounds of “proposals” until constraints satisfied. • In C-K, rejected proposals are adjusted & resubmitted

  9. C-K algorithm for parking, briefly • Iterative rounds • Parking choice • Latecomer rejection • Rejectees adjust departure time to that lot a unit-step earlier • Departure-time adjustment counts against utility • Choice may repeat • Trip “accepted” may be “bumped” in a later round • Stop when no parking oversubscribed

  10. Crawford-Knoer properties • User-optimal equilibrium • Escalation of early arrival times • Last-minute arrival rush • No denial of choice • Gradual adjustment avoids problems, can use efficient methods • Needs an early-departure utility parameter

  11. System equilibration flow Lot-Full Times

  12. Thanks! Questions, requests for reports welcomed at jag@dksassociates.com DKS Associates TRANSPORTATION SOLUTIONS www.dksassociates.com

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