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Analyzing cyclists’ behaviors and exploring the environments from cycling tracks

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Xingzhe Xie1, Wilfried Philips1, Kevin Bing-Yung Wong, Hamid Aghajan …

Xingzhe Xie1, Wilfried Philips1, Kevin Bing-Yung Wong, Hamid Aghajan
1Gent University, Belgium; Stanford University, USA
Topic: “Analyzing cyclists’ behaviors and exploring the environments from cycling tracks” - (Presenter: Nyan Bo Bo1)

Published in: Technology, Sports

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  • 1. 1Analyzing cyclists’ behaviors and exploring theenvironments from cycling tracksXingzhe Xie1, Kevin Wong2, Hamid Aghajan2and Wilfried Philips11TELIN – IPI – iMinds, Ghent University, Belgium2Electrical Engineering, Stanford University, US
  • 2. Overview•Introduction•Approach Overview– Isolating tracks from GPS data– Extracting features at different levels– Analyzing cycling behaviors– Road associations using OSM data•Results•Conclusion2
  • 3. Overview•Introduction•Approach Overview– Isolating tracks from GPS data– Extracting features at different levels– Analyzing cycling behaviors– Road associations using OSM data•Results•Conclusion3
  • 4. Dataset● All Raw GPS data for 79 cyclistsID, PersonID, Time, Latitude, Longitude, Heightm, Speed,and etc.● Cycle trips details databaseTripID, PersonID, TripMonth, TripDay, TripDate, TripDay,OriginTime, DestTime, Purpose, Dist, TimeSpent, andetc.● Participants’ socio-demographic databaseTripID, AgeGroup, HealthStatus, Employment, AHIncome,Gender, Yearof cycle
  • 5. Our goalsIsolate cycle tracks from GPS raw data,Analyze cyclists’ behaviors(both personal andshared behaviors)Explore how the tracks’ features relate tocyclists’ demographic characteristics
  • 6. Overview•Introduction•Approach Overview– Isolating tracks from GPS data– Extracting features at different levels– Analyzing cycling behaviors– Road associations using OSM data•Results•Conclusion6
  • 7. Isolating tracks from GPS dataHierarchical Database
  • 8. Extracting features at different levelsTracks features: BeginTime, EndTime, BeginLocation,EndLocation, Distance, Duration Time, Staying Segments,Average Speed and Speed Standard DeviationPersons features: Average speed, Average time duration,Average distance of all tracks with the same purposeOverall features: Average speed of people in the same agegroup, or with the same gender or health status, or cyclingyears.
  • 9. Analyzing cycling behaviorsAssumption: the two places where cyclists visit mostoften are the home and workplace (because most ofthe cyclists in our dataset bike to and from work){Starting locations, ending locations, staying locations}Home and work place are inferred based on thefrequency of the cyclists visiting these placesCyclists activities are classified to staying and movingaccording to speed. Staying locations are the placewhere the cyclist performs staying activity for morethan 10 seconds
  • 10. OSM PreliminariesOpenStreetMap (OSM): Acollaborative mapping datacontain many types of GIS dataincluding:● Road locations and names● Points of Interest● Natural Features● Bodies of Water● Political boundariesCycling GPS traces areassociated with the closest roadsegmentOSM extract of a 25 square kmsection near Newcastle upon Tyne.
  • 11. OSM Geographic ProcessingCumulative Cyclists tracks covered 4degrees of Latitude and Longitude,about 600,000 OSM road segmentsDeveloped a spatial partitioningalgorithm to search for nearest roadsegments● Used 300 longitudinal and latitudinalbins, a total of 90,000 spatial bins● Largest bin size was 600 roadsegments, a worst case speed up of1000X.Spatial bins transversed by onecyclist.
  • 12. Road associations using OSM data● Bin size chosento limit worst caseruntime● Mean of 20segments per bin.● Median bin size of4 segments● Roughly 56000out of 90000 binswere empty
  • 13. Overview•Introduction•Approach Overview– Isolating tracks from GPS data– Extracting features at different levels– Analyzing cycling behaviors– Road associations using OSM data•Results•Conclusion13
  • 14. Cycling behaviors analysisRed lines: Places where thiscyclist visits oftenThe height of red lines:thefrequency of his visit to that placeHomeWork place
  • 15. Cyclist ID Distance(km)MeanSpeed(kmh)Speed standarddeviationDuration(m:s)Purpose103 5.42 15.9 10.3 14:55 1103 5.48 16.9 8.86 11:44 1103 5.49 15.9 8.04 12:20 1103 5.78 13.8 10.1 13:11 1103 5.72 14.6 9.76 12:17 1103 1.47 7.40 6.42 9:43 1Cycling behaviors analysisTracks from home to workAbnormal routine from home to work: the last track:This cyclist goes to another place for work
  • 16. Cycling behaviors analysisMO TU WE TH FR SA SUTo work 57 62 61 55 48 4 4To visit (friends, etc) 2 1 4 7 10 6 7To work related task 5 10 9 8 5 2 0To Food shopping 5 11 5 4 10 7 8To Non-food shopping 4 1 3 5 3 3 5To School (Student) 3 5 5 4 4 2 1To Entertainment 4 3 1 6 8 5 4To Eat (Lunch, etc) 2 2 4 1 2 3 2To Home 62 65 66 58 57 23 32Other 13 11 14 4 10 5 9the relation between purpose and day
  • 17. Road associations using OSM dataCyclist Trajectory Closest Road Segments
  • 18. Finding areas with cycling anomaliesOur initial anomaly of interest is rapid starts and stops of the cyclist, show inred.
  • 19. Closest road segments to cycling anomaliesAlternative Anomaly visualization as aheatmap.● Hotter colors represent higher numbers ofanomalies.● Portions of the heatmap with no anomalieswere left as white for readability.● "Highbury" and "Jesmond DeneRoad",highlighted in red had the mostanomalies● Site of 12 high acceleration/decelerationevents
  • 20. Overview•Introduction•Approach Overview– Isolating tracks from GPS data– Extracting features at different levels– Analyzing cycling behaviors– Road associations using OSM data•Results•Conclusion20
  • 21. ConclusionWhat we did:Initial analysis about cycling behaviors and road structuresFuture work:Apply machine learning method to analyze the the relationbetween age/gender/health status and cycling featuresExplore how the environment affects cyclists’ behaviorsthrough studying the tracks and map dataUse other supplementary data could be incorporated as well,such as historic weather data for the time and place of theGPS traces along with historical traffic data
  • 22. THANKS FOR YOUR ATTENTION!Xingzhe.Xie@telin.ugent.bekbw5@stanford.edu