Transcript A New Technique for Destination Choice

A New Technique for Destination
Choice
TRB Planning
Applications Conference
Houston May 2009
William G. Allen, Jr., P.E.
Transportation Consultant
Changes in Destination Choice
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Old: gravity model
New: logit model
Old: aggregate zone-zone totals
New: individual tours
Old: both productions and attractions
constrained
New: only productions constrained
Distribution Functions
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Gravity model
Aj  Fij  Kij
Tij  Pi 
(A
j
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Logit model
P rij 
U ij
e
e
U ij
j
j
 Fij  Kij )
Discrete Tour-Based Models
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These are becoming popular
Operate on individual trips (tours)
Destination choice model creates tours
Each tour is modelled individually
Discrete Destination Choice Models
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Probability of trip from zone I going to zone J
Correct number of trips leaving zone I
Trips attracted to zone J may not match
expectations
No attraction model
What if you want to estimate zonal attraction
totals?
Travel Probability
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Gravity calculations are normally aggregate
But a gravity model can be applied in
disaggregate fashion
Probability of trip from zone I going to zone J
Stochastic selection of attraction zone,
similar to logit model
New Methodology
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Conventional model is applied stochastically
Use standard gravity formula to calculate
probabilities
Calculate trips in random order by P zone
As trips are allocated, adjust attractions
Why do this?
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Maybe the attraction model is important
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Check on production total
Jobs/housing balance
Permits comparison of aggregate vs. discrete
procedures
Stepping stone towards advanced tourbased process
Speed up existing model with many zones
Procedure
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Randomize production records by zone
For each record, gravity model calculates
probability of travel to all zones
Allocate trip to destination zone J (Monte
Carlo)
Decrease remaining attractions in J by 1
Repeat until all trips are allocated to
destinations
Software
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Conceptually simple, computationally not
Scripting in Cube possible, but run time is
long
Faster in Fortran: 90 min to 13 min
Real World Example
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Metropolitan Washington COG model of
Commercial trips
Separate models for I/I and external trips
Simple trips, not tours
Balanced trip ends by zone (orig. = dest.)
1,972 real internal zones
Totals: 901K I/I, 63K external trips
Steps
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Normal trip generation
Create 1 prod. record per trip, in random
zone order
Calculate Fij factors
Allocate each prod. record to attr. zone
Also run time of day in discrete fashion
Build trip tables from trip records
Results
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Conventional and new trip tables are very
similar
Average trip lengths: old=25.87, new=25.96
Trip length distributions almost identical
COM VMT: old=10.8 M, new=11.5 M
Distribution takes longer, assignment is
shorter
Conclusions
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“New” destination choice method developed
Destination choice step takes longer
Results comparable to conventional method
Easily expanded to other trip types
Applying an aggregate model this way is a
stepping stone to a discrete tour-based
model