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Application of gps tracking in bicycle research
 

Application of gps tracking in bicycle research

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Second keynote speaker presentation

Second keynote speaker presentation
By Hans Skov-Petersen
BIKEABILITY & University of Copenhagen, Denmark
Topic: Application of GPS tracking in bicycle research

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    Application of gps tracking in bicycle research Application of gps tracking in bicycle research Presentation Transcript

    • Photos: Inger Grønkjær Ulrik, Andre Neves and Hans Skov-Petersenwww.bikeability.dkApplication of GPS trackingin bicycle researchHans Skov-Petersenhsp@life.ku.dkGeoscience and natural resourcesUniversity of Copenhagen
    • Overview of the presentation• Basic considerations:...• Sampling locations or sampling individuals• A base-line framework for analysis of trackingdata• Motivation and field of application forinvestigations in cyclists’ route choice and andway finding behaviour• Scopes of spatial cognition and behaviour: a‘Focal‘ vs ‘Global’ approach• Data models and spatial domains in spatialbehaviour: Fields vs networks
    • Sampled or comprehensive?
    • GPS tracking:Analytical frameworkDescription InferenceLocations only Additional layersLocalIndividual pointsWhere is (x, y)?What is the PDOP of..?Distance to paths’ and points ofinterest.Where do stops occur?FocalSpatial/temporal‘window’How fast?Stop/go?How steep? Speed/slope relationsChoice of ‘next point’(relative to options)ZonalSingletrack/tours/routsHow far?Round trip?Average speed?Altitude difference?Min/max altitude along a trackLand cover distributionChoice of route (relative tooptions)GlobalAll tours, for anindividual or allrespondentsData miningSpatial/temporal clusteringArea of interestPath pressureKernel distributionOD distributionRelation of congestedlocations= Map Algebra(Dana Tomlin)
    • Path pressureDescription InferenceLocations only Additional layersLocalIndividual pointsWhere is (x, y)?What is the PDOP of..?Distance to paths’ and points ofinterest.Where do stops occur?FocalSpatial/temporalHow fast?Stop/go?How steep? Speed/slope relationsChoice of ‘next point’(relative to options)ZonalSingletrack/tours/routsHow far?Round trip?Average speed?Altitude difference?Min/max altitude along a trackLand cover distributionChoice of route (relative tooptions)GlobalAll tours, for anindividual or allrespondentsData miningSpatial/temporal clusteringArea of interestPath pressureKernel distributionRelation of congestedlocations
    • Path pressure…. Just an average GIS analysis.
    • Zonal StatisticsDescription InferenceLocations only Additional layersLocalIndividual pointsWhere is (x, y)?What is the PDOP of..?Distance to paths’ and points ofinterest.Where do stops occur?FocalSpatial/temporalHow fast?Stop/go?How steep? Speed/slope relationsChoice of ‘next point’(relative to options)ZonalSingletrack/tours/routsHow far?Round trip?Average speed?Altitude difference?Min/max altitude along a trackLand cover distributionChoice of route (relative tooptions)GlobalAll tours, for anindividual or allrespondentsData miningSpatial/temporal clusteringArea of interestPath pressureKernel distributionRelation of congestedlocations
    • Zonal statisticsEtc, etc….
    • Bikeability: GPS trip statisticsNumber of respondents 179Number of trips 1292Avg. dist 5.4 kmAvg. time 22.4 minAvg. speed 14.4 km/h
    • Speed/SlopeDescription InferenceLocations only Additional layersLocalIndividual pointsWhere is (x, y)?What is the PDOP of..?Distance to paths’ and points ofinterest.Where do stops occur?FocalSpatial/temporalHow fast?Stop/go?How steep? Speed/slope relationsChoice of ‘next point’(relative to options)ZonalSingletrack/tours/routsHow far?Round trip?Average speed?Altitude difference?Min/max altitude along a trackLand cover distributionChoice of route (relative tooptions)GlobalAll tours, for anindividual or allrespondentsData miningSpatial/temporal clusteringArea of interestPath pressureKernel distributionRelation of congestedlocations
    • Speed/Slope dependency50 summer (hikers and mountainbikers) subtracksShapeFileSubTrackDistanceFromIDToIDSpeedSlopetrip_000157_20090803.shp 1 103.2752303 1734010 1734030 3.72 3.78trip_000157_20090803.shp 1 103.3322894 1734011 1734031 3.72 5.71trip_000157_20090803.shp 1 109.8029544 1734012 1734032 3.95 5.37trip_000157_20090803.shp 1 108.3478953 1734013 1734032 4.11 6.85trip_000157_20090803.shp 1 105.0795719 1734014 1734032 4.20 7.06trip_000157_20090803.shp 1 101.4690836 1734015 1734032 4.30 7.31trip_000157_20090803.shp 1 100.4480057 1734016 1734032 4.52 7.39trip_000157_20090803.shp 1 110.8677651 1734017 1734033 4.99 6.69trip_000157_20090803.shp 1 110.1191898 1734018 1734033 5.29 6.74trip_000157_20090803.shp 1 109.7932297 1734019 1734033 5.65 6.76trip_000157_20090803.shp 1 109.4015346 1734020 1734033 6.06 6.78trip_000157_20090803.shp 1 108.4362564 1734021 1734033 6.51 6.84trip_000157_20090803.shp 1 107.7804234 1734022 1734033 7.05 6.88trip_000157_20090803.shp 1 105.8857316 1734023 1734033 7.62 7.01trip_000157_20090803.shp 1 111.1493209 1734024 1734034 8.00 7.39trip_000157_20090803.shp 1 102.0558604 1734025 1734034 8.16 6.56trip_000157_20090803.shp 1 103.5524648 1734026 1734035 8.28 6.46trip_000157_20090803.shp 1 105.4596282 1734027 1734036 8.44 10.40trip_000157_20090803.shp 1 110.0411021 1734028 1734037 8.80 9.16Speed, km/h)Slope(%)
    • Speed/slope(Zonal: Entire tours)Alpha Beta R2Fast (> 6 km/h) 9.95 -0.22 0.11Slow 4.01 -0.02 0.02
    • Speed/slope(Entire tours – Actual activities)
    • Studying wayfinding behaviourMotivation and potential fields of applicationPreference estimation and evaluation• Investigation of the relative importance ofcharacteristics to the bicycle infrastructureRoute finding• Preset impedance parameters• Incremental, personalized parameters (web2.0 style)Accessibility modeling• Assessment of anticipated effects of plannedinfrastructuresBehavior simulation• Agent-based modeling
    • Revealed PreferenceLook at what people do!To reveal preferences,behaviour has to beinvestigated in terms ofpossibilities... So it is all about choicesmade among alternativeoptions
    • 1: Do we have a perfect, ’mentalmap’ to base out choices on?2: Do we apply knowledge thatcan be perceived from ourimmediate surroundings?? ?... A ’focal’ or ’node’ scope (locomotion)... A ’global’ or ’route’ scope (wayfinding)?How do bicyclists navigate– investigation strategies
    • The ‘global’ choice experimentMap matching and choice set generation?
    • Strategies for generation ofalternative routesBased on OSM (with moderations) chosen routewas compared to …• Shortest path• A random selection of alternatives• Based on a modified labeling algorithm• Max overhead distance over chosen route: 25%• Max distance from chosen route: 1000 m• Max 20 alternatives• Two approaches:• Including Path Size (a measure of internaloverlap)• Max25: Allowing only member with lessoverlap than 25% with any other alternativein the set?
    • GPS data handling:Map matching and local choice set generation? ?
    • The Route modelsParameter Path Size Max25%ShortestpathLength -0.00433*** -0.00254*** 0.06005 ***Number of left turns -0.18323 *** -0.21797 *** 0.33594 ***Number of right turns -0.0738 ** -0.12617** 4.22133 ***% of route with Cycle track 4.66329 *** 4.68672 *** 141.683 ***Cycle lane 5.86333 *** 7.82885 *** 66.2823 ***Designated cycle track 6.20337 *** 8.72841 *** 182.773 ***Shared track 2.17781 *** 2.79813 *** 270.278 ***% of route with Artery road -2.34578 ** -4.04166 ** -88.9948 ***Minor road 0.80765 0.43654 52.9103 ***Other road (road type notspecified) 0.43071 -0.63882 84.0751 ***Road with multy story housing -0.78488 -1.42363 108.121 ***Shopping street -9.5252 -16.99 204.692 ***Log LikelihoodFunction -852 -309 -917Routes are compared to a standard situation with nobicycle facilities on a main road
    • The Focal modelQuite early results….Parameter CoefficientSignificanslevelDirections Relative angle to destination 0.0005317***Left turn -1.181548***Right turn -1.48063***Uturn -1.747325***Bicycle facilities Track 0.7465979***Lane 0.9427726***Designated track 1.140476***Shared track 0.5860569***Green Environment -0.0608228***Road type Artery road 0.7905237***Minor road 0.8029141***Other road (road type not specified) 0.5247407***Road with multi storyed housing 0.0150856*Shopping street 0.2214325***
    • The route model vs the focal model:The known vs unknown areaalA route is stated to be in a ‘unknown area’ ifmore than 50% of its points where more than250m from poinst on any other route taken bythe same respondentPseudo R2 Route modelMax25%Focal modelAlln=12910.7523 0.3679Known arean=10920.7811 0.3672Unknown arean=1990.6046 0.3743
    • The Focal modelThe first and the last 25% of of a trip wasdefined as ‘start’ and ‘end’Pseudo R2 Focal modelAll (unfortunately not ‘Middle’)n=192,3700.3679Startn=50,9530.3130Endn=42,4240.3618
    • Way forward..• Further analysis has to be performed• Focal vs Global scope for different cyclist types anddifferent cycling situations• Refinements of parameters• Reassessment of the estimates to support probabilisticlocomotion in Agent Based Models• We are aiming at four papers from the study:• Cyclists’ wayfinding and route choice (GPS/RP)• Cyclists’ wayfinding and route choice (SP)• A typology of Danish cyclists, based on mobility styles• Cyclist types applied to wayfinding
    • Spatial domains in revealed spatialchoice experimentsRestricted spatialdomain (network)Unrestricted spatialdomain(field/raster)Focal, locomotionGlobal, way finding?? ?
    • Revealed Choice experimentUnrestrictedA single pointIt’s alternativesAll points and alternatives
    • Spatial domains in revealed spatialchoice experimentsRestricted spatialdomain (network)Unrestricted spatialdomain(field/raster)Focal, locomotionGlobal, way finding?? ??
    • That’s itThanks for nowHans Skov-Petersen – hsp@life.ku.dkJette Bredahl JacobsenBernhard SnizekSuzanne Elisabeth VedelSkov & Landskab, LIFE/KUBernhard Barkow, creativeyes.atBikeability– cities for zero-emission cities and public health