application of a disaggregate quasi-dynamic model of park-and-ride lot choice
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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John GibbDKS AssociatesTransportation Solutions
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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
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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
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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
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“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 fillNo frustrated arrivals to full lots
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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
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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…
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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
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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
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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
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System equilibration flow
Lot-Full Times
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Thanks!
Questions, requests for reports welcomed at
DKS AssociatesTRANSPORTATION SOLUTIONS
www.dksassociates.com