Axhausen KW 2012 LargeScale Travel Data Sets and Route Choice Modeling European Experience presentation at Workshop 189 Route choice modeling and availability of data sets 92nd Annual Meeting of the Transportation Research Board Washington January 2013 ID: 539589
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Bevorzugter Zitierstil für diesen Vortrag
Axhausen
, K.W. (2012) Large-Scale Travel Data Sets and Route Choice Modeling: European Experience, presentation at Workshop 189 „Route choice modeling and availability of data sets“, 92nd Annual Meeting of the Transportation Research Board, Washington, January 2013. Slide2
Large-Scale Travel Data Sets and Route Choice Modeling: European ExperienceKW AxhausenIVTETHZürichJanuary
2013Slide3
3
GPS surveys and analysis:
Nadine
Rieser
–
Schüssler
Lara
Montini
Mikrozensus 2010 (Swiss national travel diaries survey)Matthias Kowald, ARE (Federal Office for Spatial Development), BernKathrin Rebmann, BfS (Federal Office for Statistics), Neuchatel
AcknowledgementsSlide4
4
What do we need ? Slide5
5Stages – trips – tours - activitiesAt homeSlide6
6Stages – trips – tours - activitiesBreakfastSlide7
7Stages – trips – tours - activities
Walk
Walk
Walk
Bus
TramSlide8
8Stages – trips – tours - activities
Work
Trip
1Slide9
9Stages – trips – tours - activities
Tour 1Slide10
10
Elements of the generalised costs of the chosen / not-chosen movement:
Duration of the stages
Routes of the stages
Circumstances of the stages (congested; parking search)
Monetary (decision relevant) costs of the stages
(Joint)
activities during the stages
Joint travel with whom Time pressure of the stagesWhat should we capture? Slide11
11
Elements of the generalised costs of the chosen/non-chosen activity:
Price levels
Price worthiness (value for money)
Social
milieu
Purpose of the activity
Joint activity:
with whom and expenditure sharing, if anyPlanning horizon of the activityWhat should we capture? Slide12
12
But how?
One week of a Zurich participantSlide13
13
But how
?
Dimension
Diary
GSM
GPS
Trips
DirectlyImpossiblePost-processingCompletenessRespondent dependentNoYes, but data loss possibleDurationRounded to the next 5, 15 minNoExactly
Destin
ations
(Exactly)
Cell tower
Exactly (?)
Purpose
Yes
No
Imputation
Company
Partially
No
No
Routes
Expensively
(Impossible)
Exactly
Recruitment
F(response
burden)
(Easy)
F(response
burden)
Response
period
1 (-42) days
(Unlimited)
(1-) 7-14 (- ) daysSlide14
Diaries14Slide15
15Format: Swiss Mikrozensus (MZ) 2010Geocoded CATI interview (LINK)Person- and household socio-demographicsStage-based travel diaryRoutes of car/motorcycle-stages were identified with two way-points (interviewer had map interface)Add-on modules for sub-samples (LINK)One-day excursionsLong-distance travelAttitudes to transport policy Integrated, but independent SC questionnaire (IVT)SC mode choiceSC route and departure time choiceSlide16
16Protocol: MZ 2010CATI with multiple calls (no incentives) (72% response rate)Households (59’971)Persons (62’868)Recruitment for the SC: 50% willingnessCustomized SCs for 85% of the recruited within 12 days70% response rate for the SC experimentsSlide17
17Route capture: Which ?
Slow modes
(Walking, cycling)
Motorised travel
(car, motorcycle)
Public transport
(train, bus, stret car…)
Stages
Roundtrips
All
All
Distance
≥
3 km
≥
3 km
≥
0 km
Detail
Capture of one way-point
Capture of two way-points
Capture of all transfers (end of stages)Slide18
18Route capture: Motorised travelSelection between fastest (blue) and shortest pathTwo way points possible (a la Google maps)Slide19
19Route capture: Public transportValid stops as start and endSelection based on the national transit schedule Distance calculation based on the rail network or shortest road path between the stops of the selected service for bussesSlide20
GPS self-tracing20Slide21
Some current examples21Captured withWhereWhatGPS loggerSwitzerlandBill
board effectiveness study (Sample > 20000)
Ireland/Austria
GPS loggers (part of EU-funded
Peacox
project)
Zürich
CantonTransit route choice (IVT, ETH)SmartphonesSingaporeCapturing activities within and outside buildings (SMART)Bay AreaCapturing trips (Joan Walker and UC Berkeley)Slide22
COST and Peacox 7th framework project: GPS based diary at IVTGPS unit:Interval: 1Hz3D positionDate and timeHPOD and other measures of accuracyAccelerometerInterval 10 Hz3D accelerationBatteryMultiple daysGSM:Savings every 4 hours on SQL database server
22Slide23
GPS-based prompted recall survey300 participant for 7 daysWeb-surveySocio-demographicsAttitudes to risk, environment, variety seekingChecking and correction of the automated processing23Slide24
Data processing24
Stages
Stops
Mode detection
map-matching
Identify stages and stops
Filtering and smoothing
Purpose imputation
Analysis and applicationSlide25
Filtering and smoothingFilteringVDOP > 5Unrealistic elevationsJumps with v > 50m/sSmoothingGauss Kernel smoother over the time axisSpeed as first derivate of the positionsRaw data are kept25Slide26
Detection of stages and stopsClusters of high densityLonger breaks without pointsAccelerometer dataGPS pointsNo movementV ≈ 0 km/h
No accelerationsMode changeWalk stage as signal
26
Stops
StagesSlide27
27
Number of trips/day in comparison with MZ 2005
Source:
Schüssler
, 2010 (without acceleration data)Slide28
28
Trip durations and length in comparison with MZ 2005
Länge
[
km]
Dauer
[min]
Source:
Schüssler
, 2010 (without acceleration data)Slide29
29
Comparison with MZ 2005
ZH
WI
GE
MZ 2005
Number of persons
2 435
1 086
1 361
2 940
Days per persons
6.99
5.96
6.51
1
Trips per day
4.50
3.40
4.26
3.65
Trip lengths [km]
7.72
7.37
7.19
8.79
Daily trip lengths [km]
34.74
23.20
29.25
32.13
Trip duration [min]
15.17
13.71
15.05
26.21
Stages per day
1.40
1.31
1.47
1.68
Source:
Schüssler
, 2010 (without acceleration data)Slide30
Mode detection30Slide31
Trip length by mode in comparison with MZ 2005
31
Walk
Car
Public Transit - City
Bycicle
Train
Source:
Schüssler
, 2010 (without acceleration data)Slide32
Map matchingCar and bike stagesSelection from a set of possible routesAt nodes all possible on-going links become new candidate routes (branches)If the tree has enough branches, it is pruned based on their total errorsEach candidate branch is assessed bySquared error between GPS and pathDeviation between GPS speed and posted speedsTransit-stagesRoute identified as for carsLine identified by time table32Slide33
Map-Matching: Number of branches against computing times
33
Source:
Schüssler
, 2010 (without acceleration data)Slide34
34
Research needs: GPS post processing
Test dataset with true values
Further integration between map-matching and mode detection
Better imputation of the movement during signal loss
More and better purpose imputation (POI – data base; land use data; frequency over multiple days)Slide35
35
What now ? Cost of GPS/Smartphone studies as a function
Rate of usable addresses
Recruitment rate
Response rate
Rate of usable returns
Correlation between household members
Correlation between
daysCorrelation between tours of a dayCorrelation between trips of tourNumber of waves with the GPS loggers Rate of loss of the GPS loggersSlide36
36What now ? DiariesGPS-self tracingAdvantagesAll variables
Social contacts
All
movements (but data loss)
Exact times, routes, locations
Longer observation periods
Dis-advantages
≈15%
under reporting of tripsRounded timesApproximate routes onlyDecreasing response rates / expensive responsePost-processing and imputation(Effort of the post-processing)Unknown response rates(Costs of the units and their distribution/collection)Slide37
37
www.ivt.ethz.ch
www.matsim.org
www.futurecities.ethz.ch
Questions
?Slide38
Map-Matching: First branches
38
Source:
Schüssler
, 2010 (without acceleration data)