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Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study

Author

Listed:
  • Chen, Cynthia
  • Gong, Hongmian
  • Lawson, Catherine
  • Bialostozky, Evan

Abstract

The combination of increasing challenges in administering household travel surveys and advances in global positioning systems (GPS)/geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extreme dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.

Suggested Citation

  • Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
  • Handle: RePEc:eee:transa:v:44:y:2010:i:10:p:830-840
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hahnel, Ulf J.J. & Gölz, Sebastian & Spada, Hans, 2013. "How accurate are drivers’ predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 123-131.
    2. repec:eee:jotrge:v:68:y:2018:i:c:p:78-93 is not listed on IDEAS
    3. repec:gam:jsusta:v:9:y:2017:i:9:p:1598-:d:111236 is not listed on IDEAS
    4. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    5. repec:gam:jsusta:v:10:y:2018:i:7:p:2351-:d:156561 is not listed on IDEAS
    6. Yijing Lu & Lei Zhang, 2015. "Imputing trip purposes for long-distance travel," Transportation, Springer, vol. 42(4), pages 581-595, July.
    7. Firnkorn, Jörg, 2012. "Triangulation of two methods measuring the impacts of a free-floating carsharing system in Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1654-1672.
    8. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.

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