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Mining Mobile Sensor Data for
Transportation Modelling
Peter Widhalm
Transportation Modelling
Goal: optimizing the transporation system by
understanding, predicting and influencing travel beh...
Motivation
Traditional household surveys
expensive
limited sample size
no up-to-date data
data quality / incomplete data
l...
IT support for collecting trip data
5
ssive active
Smartphone AppNetwork Traffic
Cell Phone Data
SEMAPHORE
6
assive active
Smartphone AppNetwork Traffic
Cell Phone Data
GPS
8:00 am
8:12 am
8:24 am
8:29 am
Advantages:
large sample...
Extraction of activity times and locations
7
Traffic flows
8
Traffic flows (commuters)
9
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 C...
Combination with other data sources
11
Shop
WorkHome
Leisure
1
2
3
45
temporal
patterns
phic context
se, POIs)
sociodemogr...
Take home message
Travel data collected with Smartphone Apps can assist traditional mobility
surveys.
Passively collected ...
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AIT Safety & Security Department AIT R&D

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Copyright Peter Widhalm at CeDEM14

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Transcript of "AIT Safety & Security Department AIT R&D"

  1. 1. Mining Mobile Sensor Data for Transportation Modelling Peter Widhalm
  2. 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. 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
  4. 4. IT support for collecting trip data
  5. 5. 5 ssive active Smartphone AppNetwork Traffic Cell Phone Data SEMAPHORE
  6. 6. 6 assive active Smartphone AppNetwork Traffic Cell Phone Data 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. 7. Extraction of activity times and locations 7
  8. 8. Traffic flows 8
  9. 9. Traffic flows (commuters) 9
  10. 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. 11. Combination with other data sources 11 Shop WorkHome Leisure 1 2 3 45 temporal patterns phic context se, POIs) sociodemographics census data
  12. 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
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