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Mining Mobile Sensor Data for Transportation Modelling

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Copyright Peter Widhalm at CeDEM14

Copyright Peter Widhalm at CeDEM14


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  • 1. Mining Mobile Sensor Data for Transportation Modelling Peter Widhalm
  • 2. Transportation Modelling Goal: optimizing the transporation system by understanding, predicting and influencing travel behavior accurate modeling of the effects of different policies before implementation 2 Policy decisions regarding the transportation system have a tangible impact on many people!
  • 3. Motivation Traditional household surveys expensive limited sample size no up-to-date data data quality / incomplete data last nation-wide survey conducted in 1995! IT support for mobility surveys goals: reduce costs, improve data quality collect trip data automatically: • Smartphones / GPS tracker • Cellular network 3 GPS
  • 4. IT support for collecting trip data 4 GPS analyze trip data: Web application 8:00 am 8:12 am 8:24 am 8:29 am
  • 5. 5 passive active Smartphone AppNetwork Traffic Cell Phone Data SEMAPHORE GPS 8:00 am 8:12 am 8:24 am 8:29 am
  • 6. 6 passive active Smartphone AppNetwork Traffic Cell Phone Data SEMAPHORE GPS 8:00 am 8:12 am 8:24 am 8:29 am Advantages: large sample size no recruiting no burden on individuals infrastructure already in place Challenges: −low spatial resolution and sparse sampling of movements −not linked to sociodemographics −not linked to purposes / activities
  • 7. Extraction of activity times and locations 7
  • 8. Traffic flows 8
  • 9. Traffic flows (commuters) 9
  • 10. Extraction of activity times and locations 10 0 0.1 0.2 0.3 0.4 2 0 3 4 4 4 5 4 5 5 ViennaCell Phone ViennaSurvey Boston Cell Phone Boston Survey ViennaCell Phone ViennaSurvey Boston Cell Phone Boston Survey
  • 11. Combination with other data sources 11 11 Shop WorkHome Leisure 1 2 3 45 temporal patterns geographic context (land use, POIs) sociodemographics census data
  • 12. Take home message Travel data collected with Smartphone Apps can assist traditional mobility surveys. Passively collected cell phone data are generated by infrastructure already in place. By combining cell phone data with other data sources such as census data, points-of-interest, land use they provide rich and up-to-date information about human travel behavior. Cell phone data help us to predict the effects of policies before implementation and to evaluate their effect after implementation 12