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A Trip Reconstruction Tool for GPS-based Personal Travel Surveys

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  • Eui-Hwan Chung
  • Amer Shalaby

Abstract

This article reports on the development of a trip reconstruction software tool for use in GPS-based personal travel surveys. Specifically, the tool enables the automatic processing of GPS traces of individual survey respondents in order to identify the road links traveled and modes used by each respondent for individual trips. Identifying the links is based on a conventional GIS-based map-matching algorithm and identifying the modes is a rule-based algorithm using attributes of four modes (walk, bicycle, bus and passenger-car). The tool was evaluated using GPS travel data collected for the study and a multi-modal transportation network model of downtown Toronto. The results show that the tool correctly detected about 79% of all links traveled and 92% of all trip modes.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:transp:v:28:y:2005:i:5:p:381-401
    DOI: 10.1080/03081060500322599
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    Citations

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    Cited by:

    1. Downs, Joni A. & Horner, Mark W., 2012. "Probabilistic potential path trees for visualizing and analyzing vehicle tracking data," Journal of Transport Geography, Elsevier, vol. 23(C), pages 72-80.
    2. Yun Xiang & Chengcheng Xu & Weijie Yu & Shuyi Wang & Xuedong Hua & Wei Wang, 2019. "Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    3. Antonio Comi & Antonio Polimeni, 2022. "Estimating Path Choice Models through Floating Car Data," Forecasting, MDPI, vol. 4(2), pages 1-13, June.
    4. 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.
    5. Rajat Verma & Eunhan Ka & Satish V. Ukkusuri, 2024. "Long-term forecasts of statewide travel demand patterns using large-scale mobile phone GPS data: A case study of Indiana," Papers 2404.13211, arXiv.org.
    6. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    7. Satomi Kimijima & Masahiko Nagai, 2017. "Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    8. Adrian C. Prelipcean & Gyözö Gidófalvi & Yusak O. Susilo, 2017. "Transportation mode detection – an in-depth review of applicability and reliability," Transport Reviews, Taylor & Francis Journals, vol. 37(4), pages 442-464, July.
    9. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    10. 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.
    11. Moiseeva, Anastasia & Timmermans, Harry, 2010. "Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning," Journal of Retailing and Consumer Services, Elsevier, vol. 17(3), pages 189-199.
    12. Nour, Akram & Hellinga, Bruce & Casello, Jeffrey, 2016. "Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS," Journal of Transport Geography, Elsevier, vol. 51(C), pages 36-44.
    13. Papinski, Dominik & Scott, Darren M., 2011. "A GIS-based toolkit for route choice analysis," Journal of Transport Geography, Elsevier, vol. 19(3), pages 434-442.
    14. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    15. Martina Erdelić & Tonči Carić & Tomislav Erdelić & Leo Tišljarić, 2022. "Transition State Matrices Approach for Trajectory Segmentation Based on Transport Mode Change Criteria," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    16. 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.
    17. Reinau, Kristian Hegner & Harder, Henrik & Weber, Michael, 2015. "The SMS–GPS-Trip method: A new method for collecting trip information in travel behavior research," Telecommunications Policy, Elsevier, vol. 39(3), pages 363-373.
    18. 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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