Extraction of bicycle commuter trips from day long gps trajectories

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Gerald Richter, Christian Rudloff, Anita Graser
Austrian Institute of Technology, Austria
Topic: “Extraction of bicycle commuter trips from day-long GPS trajectories”

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Extraction of bicycle commuter trips from day long gps trajectories

  1. 1. Extraction of bicycle commuter tripsfrom day-long GPS trajectoriesCycling Data Challenge 2013Leuven, Belgiumworkshop presentationGerald Richter 1 Christian Rudloff 1 Anita Graser 11Austrian Inst. of Technology – Mobility Dept. – Dynamic Transportation SystemsG. Richter | AIT | mobility | DTS May 14, 2013 1 / 19
  2. 2. The Austrian Institute of TechnologyAIT – who we are and what we doAustria’s largest non-university research instituteAIT: 5 departments focussing on applied research topics• Energy• Mobilitybusiness units:• Transportation Infrastructure Technologies• Dynamic Transportation Systems• Electric Drive Technologies• Light Metals Technologies Ranshofen• Safety & Security• Health & Environment• Foresight & Policy DevelopmentG. Richter | AIT | mobility | DTS May 14, 2013 2 / 19
  3. 3. Dynamic Transportation Systems“develop efficient, safe and cost-effective multimodaltransportation solutions for transportation networks, hubs andservices”Airports / Train StationsShopping Centres / EventsMulti-Modal TransportationNetworksTransport LogisticsCrowd Dynamics Traffic Flow Modelling Dynamic Vehicle RoutingOptimisationSimulation /PredictionData AnalysisData CollectionG. Richter | AIT | mobility | DTS May 14, 2013 3 / 19
  4. 4. GPS measurementsand some peculiaritiesProper GPS measurement requires 4 satelitesto be visible by device.Measurement is stochastic process by nature.Positional precision is gaussian distributedunder clear-view conditions.Additional effects arise from obstructed view(signal shadowing, reflection by obstacles).• outliers: sudden change in signal receptionconditions• drift: longer phases of signal impairment,receiver-internal error correction walking amisguided path.snap-backtrue pathG. Richter | AIT | mobility | DTS May 14, 2013 4 / 19
  5. 5. The input data. . . hence this initial situationsome points not out of thisworldsome tracks far outside theregion of interestmost likely due to GPSinitialisation phase– fixable by bounding boxclippingFigure: detail UKG. Richter | AIT | mobility | DTS May 14, 2013 5 / 19
  6. 6. A simple yet efficient approachstages of processingCleaning• Outliers and unlikely points in the data are removedi.e.: some trajectory smoothness is ascertained• Data is split into trip trajectories inbetween stops oractivitiesi.e.: a journey’s segments are identifiedMode Detection• A training set of data is used to identify decision criteriawithin a manually chosen set of variables (trip parameters).• With those criteria modes of trips are detected to separatebike trips from other tripsDetails found in [1, 3, 2]G. Richter | AIT | mobility | DTS May 14, 2013 6 / 19
  7. 7. Cleaning the dataSteps of the data cleaning algorithmOutliers are removed according to• geographic location: within bounding box around area ofinterest• accesiblity: reachable by realistic speeds (here ≤ 50 ms )• GPS drifts: points before trajectory snap-backs are deleteduntil the remaining trajectory only contains realistic speedsStop detection and trip separation• Stop is detected when trajectory does notleave circle of radius 30m for at least 5minutes.• GPS trajectories are cut into trips at stoppoints (removal of tumbleweed)• Next trip starts when trajectory leavescircleG. Richter | AIT | mobility | DTS May 14, 2013 7 / 19
  8. 8. Unlikely pointsTumbleweed also found atshorter stops (e.g. traffic lights)Removed by loop detection(look ahead 3 minutes andfind very low effectivevelocities to reach asuccessive trajectory pointin given time interval)All points in loop arereplaced by one middlepoint between start andend of loop.G. Richter | AIT | mobility | DTS May 14, 2013 8 / 19
  9. 9. Modal DecisionprincipleClassification of cycling tracksusing a decision treeOther methodologies (logisticregression, support vectormachines, neural network)show similar out of sampleperformanceDecision tree are easy to useand interpretexemplary diagram:(2-dimensional feature space)Training data from the Vienna region with 8 different modesG. Richter | AIT | mobility | DTS May 14, 2013 9 / 19
  10. 10. Mode Detectionalgorithmic choicesFor CDC data set distinction was made between 3 ModesWalkingCyclingOtherAlgorithmic separability optimisation left 3 separation variables:maximum velocitypercentage of time over 16 km/hmaximum accelerationG. Richter | AIT | mobility | DTS May 14, 2013 10 / 19
  11. 11. Processing outcomevisuallyblack: refined tracks; green: processed and detected cycling tracksG. Richter | AIT | mobility | DTS May 14, 2013 11 / 19
  12. 12. Bird’s eye comparisonin numbersComparison of no. cycle trips and trip lengthrefined all modes cyclingNo. cycle trips 941 1,734 749Total trip [km] 4,483 6,800 3,014Oct 12 2011Oct 19 2011Oct 26 2011Nov 02 2011Nov 09 2011Nov 16 2011Nov 23 2011020000400006000080000100000totaltriptime[s]trips per day comparisonwrt. total timediaryprocessedOct 12 2011Oct 19 2011Oct 26 2011Nov 02 2011Nov 09 2011Nov 16 2011Nov 23 2011010203040506070totalnumberoftripstrips per day comparisonwrt. number of tripsdiaryprocessedG. Richter | AIT | mobility | DTS May 14, 2013 12 / 19
  13. 13. Comparing track densitiesprinciplefewer trips weredetected than in refineddataalgorithm unlikely tofalsely qualify tracks ascyclingcoordinate shift in initialdata along thebackslash diagonal(processed cycling trips) – (refined trips)G. Richter | AIT | mobility | DTS May 14, 2013 13 / 19
  14. 14. Different cyclists0 100 200 300 400 500 600 700avg. number of pts per trip50510152025numberoftripsprocessed trip scatterfor all cyclistsquite different profilesby cycling habit ortrajectory cleaning?⇒ look associatedvelocity profiles0 10 20 30 40 50 60speed [km/h]0100200300400500#GPSpointsspeed distribution: cyclist 101(high number of trips)0 10 20 30 40 50 60speed [km/h]050100150200250300350400450#GPSpointsspeed distribution: cyclist 113(high avg. number of points per trip)G. Richter | AIT | mobility | DTS May 14, 2013 14 / 19
  15. 15. Cyclist differences on maphigh number of points per trackcyclist 113high number of trackscyclist 101G. Richter | AIT | mobility | DTS May 14, 2013 15 / 19
  16. 16. Big visualG. Richter | AIT | mobility | DTS May 14, 2013 16 / 19
  17. 17. Summary & conclusionsApplied methods successfully discern useful GPS tracking datafrom technological artifacts.Not too complex methods, good classification of the cyclingtransport modeResults display periodic features of protocolled travel activity wrt.number of trips and travel times.Algorithm cannot identify all cycling tracks of reference data.Differences most likely due to dissimilar training set.Low rate of false modal identification for cycling, while retainingthe substantial part of useable tracking data.Compared to reference data, removal of erratic GPSmeasurement errors with appreciable reliability.TODO: Use of homologous training data (road network topologyand traffic densities) expected to yield consistently better results.G. Richter | AIT | mobility | DTS May 14, 2013 17 / 19
  18. 18. RemarksThanks to:CDC2013 organisersThe other contributers and colleagues who I work with. . . a patient audienceQuestions & comments to:Gerald.Richter@ait.ac.atChristian.Rudloff@ait.ac.atAnita.Graser@ait.ac.atG. Richter | AIT | mobility | DTS May 14, 2013 18 / 19
  19. 19. References[1] D. Bauer et al. “On Extracting Commuter Information fromGPS Motion Data”. In: Proceedings InternationalWorkshop on Computational Transportation Science(IWCTS08). 2008.[2] R. Hariharan and K. Toyama. “Project Lachesis: Parsingand Modeling Location Histories.” In: Proceedings of theThird International Conference on GIScience. Adelphi,MD, USA, 2004.[3] C. Rudloff and M. Ray. “Detecting Travel Modes andProfiling Commuter Routes Solely Based on GPS Data”.In: TRB 89th Annual Meeting. 2010.G. Richter | AIT | mobility | DTS May 14, 2013 19 / 19

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