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Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python

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  • Ron Dalumpines
  • Darren M. Scott

Abstract

The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.

Suggested Citation

  • Ron Dalumpines & Darren M. Scott, 2017. "Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(5), pages 523-539, July.
    • Handle: RePEc:taf:transp:v:40:y:2017:i:5:p:523-539
    DOI: 10.1080/03081060.2017.1314502
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    References listed on IDEAS

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    1. Millward, Hugh & Spinney, Jamie, 2011. "Time use, travel behavior, and the rural–urban continuum: results from the Halifax STAR project," Journal of Transport Geography, Elsevier, vol. 19(1), pages 51-58.
    2. Hausman, Jerry & McFadden, Daniel, 1984. "Specification Tests for the Multinomial Logit Model," Econometrica, Econometric Society, vol. 52(5), pages 1219-1240, September.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    4. Eui-Hwan Chung & Amer Shalaby, 2005. "A Trip Reconstruction Tool for GPS-based Personal Travel Surveys," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(5), pages 381-401, August.
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    1. Sun, Zhiyuan & Wang, Duo & Wang, Jianyu & Han, Lu & Xing, Yuxuan & Lu, Huapu & Chen, Yanyan, 2024. "Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).

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