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Identifying travel mode with GPS data

Author

Listed:
  • Fang Zong
  • Yixin Yuan
  • Jianfeng Liu
  • Yu Bai
  • Yanan He

Abstract

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.

Suggested Citation

  • Fang Zong & Yixin Yuan & Jianfeng Liu & Yu Bai & Yanan He, 2017. "Identifying travel mode with GPS data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(2), pages 242-255, February.
    • Handle: RePEc:taf:transp:v:40:y:2017:i:2:p:242-255
    DOI: 10.1080/03081060.2016.1266170
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    Cited by:

    1. Broach, Joseph & Dill, Jennifer & McNeil, Nathan Winslow, 2019. "Travel mode imputation using GPS and accelerometer data from a multi-day travel survey," Journal of Transport Geography, Elsevier, vol. 78(C), pages 194-204.
    2. Kemajou, Armel & Jaligot, Rémi & Bosch, Martí & Chenal, Jérôme, 2019. "Assessing motorcycle taxi activity in Cameroon using GPS devices," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.

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