A Disaggregate Quasi-Dynamic Park-and
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Transcript A Disaggregate Quasi-Dynamic Park-and
APPLICATION OF A DISAGGREGATE
QUASI-DYNAMIC MODEL OF PARKAND-RIDE LOT CHOICE
John Gibb
DKS Associates
Transportation Solutions
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
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
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
“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
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
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…
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
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
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
System equilibration flow
Skim Matrices
Network
Assignment
P&R lot
placement
Lot-Full
Times
Activity-Based
Demand Model
Trips
Thanks!
Questions, requests for reports welcomed at
[email protected]
DKS Associates
TRANSPORTATION SOLUTIONS
www.dksassociates.com