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Analyzing cyclists’ behaviors and exploring the environments from cycling tracks

Xingzhe Xie1, Kevin Wong2, Hamid Aghajan2 and Wilfried Philips1

1TELIN – IPI – iMinds, Ghent University, Belgium2Electrical Engineering, Stanford University, US

Overview

• Introduction• Approach Overview

– Isolating tracks from GPS data

– Extracting features at different levels

– Analyzing cycling behaviors

– Road associations using OSM data

• Results• Conclusion

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Overview

• Introduction• Approach Overview

– Isolating tracks from GPS data

– Extracting features at different levels

– Analyzing cycling behaviors

– Road associations using OSM data

• Results• Conclusion

3

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, and etc.

● Participants’ socio-demographic databaseTripID, AgeGroup, HealthStatus, Employment, AHIncome, Gender, Yearof cycle

Our goals

Isolate cycle tracks from GPS raw data,

Analyze cyclists’ behaviors(both personal and shared behaviors)

Explore how the tracks’ features relate to cyclists’ demographic characteristics

Overview

• Introduction• Approach Overview

– Isolating tracks from GPS data

– Extracting features at different levels

– Analyzing cycling behaviors

– Road associations using OSM data

• Results• Conclusion

6

Isolating tracks from GPS data

Hierarchical Database

Extracting features at different levels

Tracks' features: BeginTime, EndTime, BeginLocation, EndLocation, Distance, Duration Time, Staying Segments, Average Speed and Speed Standard Deviation

Persons' features: Average speed, Average time duration, Average distance of all tracks with the same purpose

Overall features: Average speed of people in the same age group, or with the same gender or health status, or cycling years.

Analyzing cycling behaviors

Assumption: the two places where cyclists visit most often are the home and workplace (because most of the cyclists in our dataset bike to and from work)

{Starting locations, ending locations, staying locations}Home and work place are inferred based on the frequency of the cyclists visiting these places

Cyclist's activities are classified to staying and moving according to speed. Staying locations are the place where the cyclist performs staying activity for more than 10 seconds

OSM Preliminaries

OpenStreetMap (OSM): A collaborative mapping data contain many types of GIS data including:

● Road locations and names● Points of Interest● Natural Features● Bodies of Water● Political boundaries

Cycling GPS traces are associated with the closest road segment

OSM extract of a 25 square km section near Newcastle upon Tyne.

OSM Geographic Processing

Cumulative Cyclists tracks covered 4 degrees of Latitude and Longitude, about 600,000 OSM road segments

Developed a spatial partitioning algorithm to search for nearest road segments● Used 300 longitudinal and latitudinal

bins, a total of 90,000 spatial bins● Largest bin size was 600 road

segments, a worst case speed up of 1000X.

Spatial bins transversed by one cyclist.

Road associations using OSM data

● Bin size chosen to limit worst case runtime

● Mean of 20 segments per bin.

● Median bin size of 4 segments

● Roughly 56000 out of 90000 bins were empty

Overview

• Introduction• Approach Overview

– Isolating tracks from GPS data

– Extracting features at different levels

– Analyzing cycling behaviors

– Road associations using OSM data

• Results• Conclusion

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Cycling behaviors analysis

Red lines: Places where this cyclist visits often

The height of red lines:the frequency of his visit to that place

Home

Work place

Cyclist ID Distance(km)

Mean Speed(kmh)

Speed standard deviation

Duration(m:s)

Purpose

103 5.42 15.9 10.3 14:55 1

103 5.48 16.9 8.86 11:44 1

103 5.49 15.9 8.04 12:20 1

103 5.78 13.8 10.1 13:11 1

103 5.72 14.6 9.76 12:17 1

103 1.47 7.40 6.42 9:43 1

Cycling behaviors analysis

Tracks from home to work

Abnormal routine from home to work: the last track: This cyclist goes to another place for work

Cycling behaviors analysis

MO TU WE TH FR SA SU

To work 57 62 61 55 48 4 4

To visit (friends, etc) 2 1 4 7 10 6 7

To work related task 5 10 9 8 5 2 0

To Food shopping 5 11 5 4 10 7 8

To Non-food shopping 4 1 3 5 3 3 5

To School (Student) 3 5 5 4 4 2 1

To Entertainment 4 3 1 6 8 5 4

To Eat (Lunch, etc) 2 2 4 1 2 3 2

To Home 62 65 66 58 57 23 32

Other 13 11 14 4 10 5 9

the relation between purpose and day

Road associations using OSM data

Cyclist Trajectory Closest Road Segments

Finding areas with cycling anomalies

Our initial anomaly of interest is rapid starts and stops of the cyclist, show in red.

Closest road segments to cycling anomalies

Alternative Anomaly visualization as a heatmap.

● Hotter colors represent higher numbers of anomalies.

● Portions of the heatmap with no anomalies were left as white for readability.

● "Highbury" and "Jesmond Dene Road",highlighted in red had the most anomalies

● Site of 12 high acceleration/deceleration events

Overview

• Introduction• Approach Overview

– Isolating tracks from GPS data

– Extracting features at different levels

– Analyzing cycling behaviors

– Road associations using OSM data

• Results• Conclusion

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Conclusion

What we did:Initial analysis about cycling behaviors and road structures

Future work:Apply machine learning method to analyze the the relation between age/gender/health status and cycling features

Explore how the environment affects cyclists’ behaviors through studying the tracks and map data

Use other supplementary data could be incorporated as well, such as historic weather data for the time and place of the GPS traces along with historical traffic data

THANKS FOR YOUR ATTENTION!Xingzhe.Xie@telin.ugent.be

kbw5@stanford.edu