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Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues

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  • Du, Jianhe
  • Aultman-Hall, Lisa

Abstract

With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.

Suggested Citation

  • Du, Jianhe & Aultman-Hall, Lisa, 2007. "Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 220-232, March.
    • Handle: RePEc:eee:transa:v:41:y:2007:i:3:p:220-232
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    Cited by:

    1. Gingerich, Kevin & Maoh, Hanna & Anderson, William, 2016. "Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319310, Transportation Research Forum.
    2. Gingerich, Kevin & Maoh, Hanna, 2019. "The role of airport proximity on warehouse location and associated truck trips: Evidence from Toronto, Ontario," Journal of Transport Geography, Elsevier, vol. 74(C), pages 97-109.
    3. Joubert, Johan W. & Meintjes, Sumarie, 2015. "Repeatability & reproducibility: Implications of using GPS data for freight activity chains," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 81-92.
    4. Antonio Comi & Antonio Polimeni, 2022. "Estimating Path Choice Models through Floating Car Data," Forecasting, MDPI, vol. 4(2), pages 1-13, June.
    5. 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.
    6. Laranjeiro, Patrícia F. & Merchán, Daniel & Godoy, Leonardo A. & Giannotti, Mariana & Yoshizaki, Hugo T.Y. & Winkenbach, Matthias & Cunha, Claudio B., 2019. "Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: The case of São Paulo, Brazil," Journal of Transport Geography, Elsevier, vol. 76(C), pages 114-129.
    7. Feng, Xiaoyan & Sun, Huijun & Wu, Jianjun & Liu, Zhiyuan & Lv, Ying, 2020. "Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 190-203.
    8. Tao Feng & Harry J.P. Timmermans, 2016. "Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(2), pages 180-194, March.
    9. Mehrdad Bagheri & Miloš N. Mladenović & Iisakki Kosonen & Jukka K. Nurminen, 2020. "Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data," Sustainability, MDPI, vol. 12(15), pages 1-26, July.
    10. Ying Hui & Mengtao Ding & Kun Zheng & Dong Lou, 2017. "Observing Trip Chain Characteristics of Round-Trip Carsharing Users in China: A Case Study Based on GPS Data in Hangzhou City," Sustainability, MDPI, vol. 9(6), pages 1-15, June.
    11. Sharma, Ishant & Mishra, Sabyasachee & Kabiri, Aliakbar & Ghader, Sepehr & Zhang, Lei, 2024. "Use of passive data for determining link level long distance trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    12. Nadine Rieser-Schüssler & Kay W. Axhausen, 2014. "Self-tracing and reporting: state of the art in the capture of revealed behaviour," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 6, pages 131-151, Edward Elgar Publishing.
    13. Yijing Lu & Lei Zhang, 2015. "Imputing trip purposes for long-distance travel," Transportation, Springer, vol. 42(4), pages 581-595, July.
    14. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.

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