A GPS-based Bicycle Route CHoice Model for San Francisco, California
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Transcript of A GPS-based Bicycle Route CHoice Model for San Francisco, California
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
A GPS-based Bicycle Route Choice Model for San Francisco, California
Jeff HoodUniversity of California, Berkeley
Billy Charlton, Elizabeth Sall, Michael SchwartzSan Francisco County Transportation Authority
Matt PaulMoPimp Productions
3 rd Conference on Innovations in Travel Demand Modeling, Tempe, Arizona
May 10, 2010
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
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•------•------•------ Introduction
CycleTracks for iPhone & Android
Choice set generation
Model estimation & validation
Trip assignment & next steps
Outline
Data processing & participants
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Why model bicycle route choice?
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CycleTracks for iPhone & Android
Conventional GPS survey
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… …
CycleTracks smartphone app
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Trip 1
Trip 2…… …Trip
n……………
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OK, it’s real pretty; Now, how do we get users?
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Data Processing & Participants
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GPS post-processing (Schüssler & Axhausen 2009)
Gaussian smoothing
Activity & mode detection
Map matching
5,178 traces497 users
3,034 bike stages
366 usersh
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Participants
CycleTracks BATS z -statp -value
(N=366) (N=153)Age
Mean 34 33 1.1 0.31
GenderFemale 21% 36% –3.5 0.00
Cycling FrequencyDaily 60% N/ASeveral times per week 34%Several times per month 7%Less than once a month 0%
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Choice Set Generation
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Existing methods
Only shortest path searches work in large networks
Link elimination(k shortest paths)
Stochastic path search
Labeledpaths
Doubly stochastic(Bovy & Fiorenzo-Catalano 2007)
The doubly stochastic method
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Link-additive path attribute
vector
Attribute i
Attr
ibut
e j
?
1. Select random coefficient vector
2. Calculate generalized cost for each link
3. Randomize link costs
4. Find shortest path
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Extract unbiased priors from the network
Reference attribute (β1=1)
Eval
uatio
n at
trib
ute
Parameter Space
Start with large boundary intervalBinary search
Eval
uatio
n at
trib
ute
Coefficient of evaluation attribute
Attribute Space
Value of evaluation attribute in
shortest path
Results of prior extraction
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Significant in estimation
Retained for degree of freedom
96 Link Elimination Routes
96 Doubly Stochastic Routes
Better
Better
Benchmarking
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MethodDoubly
StochasticLink
Elimination
No. parameters 32 --
No. link randomizations 3 --
No. unique routes 76 (avg.) 96
Search algorithm Dijkstra Euclidean A*
Computing time 3 h 46 m 8 h 06 m
2,678 observations, 4 CPUs, coded in Python
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Model Estimation & Validation
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Estimation results
Attribute Coef. SE t -stat. p -val.Length (mi) --1.05 0.09 --11.80 0.00Turns per mile --0.21 0.02 --12.15 0.00Prop. wrong way --13.30 0.67 --19.87 0.00Prop. bike paths 1.89 0.31 6.17 0.00Prop. bike lanes 2.15 0.12 17.69 0.00 Cycling freq. < several per wk.1.85 0.04 44.94 0.00Prop. bike routes 0.35 0.11 3.14 0.00Avg. up-slope (ft/100ft) --0.50 0.08 --6.35 0.00 Female --0.96 0.22 --4.34 0.00 Commute --0.90 0.11 --8.21 0.00Log(path size) 1.07 0.04 26.38 0.00
2,678 weighted observations, ρ2 = 0.28
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Average marginal rates of substitution
MRS of Length on street for ValueUnits
Length on bike paths 0.57 noneLength on bike lanes 0.49 noneLength on bike routes 0.92 noneLength wrong way 4.02 noneTurns 0.10 mi/turnTotal rise 1.12 mi/100ft
User benefit of bike lanes: $0.98 per mile per trip
Better
15% exactpredictions
Better
Calibrated choice set
Best generatedOverlap: 85%Dissimilarity: 0.23PS-normed prob: 0.022
PredictedOverlap: 5%Dissimilarity: 0.83PS-normed prob: 0.025
Example
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Trip Assignment & Next Steps
AM AssignmentComputing Time:12 h 35 m
Bicycles per hour
20 360
0 180
Bike Route Choice Set Generation
Final Bicycle Assignnment
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Next steps for SF-CHAMP
Initial Road Assignnment
Roadway & Transit Skimming
Work Loc. & Destination Choice
Full Day Tour Generation
Tour & TripMode Choice
Final Road & Transit Assignment
Population Synthesizer
Vehicle Availability
Bicycle AvailabilityLogsums
Logsums
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Acknowledgements
ContactJeff Hood, UC Berkeley: [email protected]
Billy Charlton, SFCTA: [email protected]
Matt Paul, MoPimp Productions: www.mopimp.com
Nadine Schüssler Kay Axhausen
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•------•------•------ Introduction
CycleTracks for iPhone & Android
Choice set generation
Model estimation & validation
Trip assignment & next steps
Recap
Data processing & participants