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Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data

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  • Dominik Papinski
  • Darren M Scott

Abstract

This paper proposes a new way to study commuting efficiency based on the degree to which actual routes between places deviate from shortest paths. Factors influencing ‘route choice efficiency’ are investigated using a sample of home-based work trips obtained from the Space—Time Activity Research (STAR) survey, which was conducted in Halifax, Canada, between April 2007 and May 2008. The findings indicate that many drivers pursue suboptimal routes. Furthermore, the results from two ordered probit models reveal that sex, household size, work schedule, actual distance to work, distance to the central business district, travel time, and exposure to public transit are among the factors found to influence route choice efficiency.

Suggested Citation

  • Dominik Papinski & Darren M Scott, 2013. "Route Choice Efficiency: An Investigation of Home-To-Work Trips Using GPS Data," Environment and Planning A, , vol. 45(2), pages 263-275, February.
    • Handle: RePEc:sae:envira:v:45:y:2013:i:2:p:263-275
    DOI: 10.1068/a44545
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    References listed on IDEAS

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    1. Kang‐Rae Ma & David Banister, 2006. "Excess Commuting: A Critical Review," Transport Reviews, Taylor & Francis Journals, vol. 26(6), pages 749-767, May.
    2. Sean Doherty & Eric Miller, 2000. "A computerized household activity scheduling survey," Transportation, Springer, vol. 27(1), pages 75-97, February.
    3. Genevieve Giuliano & Kenneth A. Small, 1993. "Is the Journey to Work Explained by Urban Structure?," Urban Studies, Urban Studies Journal Limited, vol. 30(9), pages 1485-1500, November.
    4. Shlomo Bekhor & Moshe Ben-Akiva & M. Ramming, 2006. "Evaluation of choice set generation algorithms for route choice models," Annals of Operations Research, Springer, vol. 144(1), pages 235-247, April.
    5. Handy, Susan & Weston, Lisa & Mokhtarian, Patricia L., 2005. "Driving by choice or necessity?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 183-203.
    6. Morton E O'Kelly & Wook Lee, 2005. "Disaggregate Journey-to-Work Data: Implications for Excess Commuting and Jobs–Housing Balance," Environment and Planning A, , vol. 37(12), pages 2233-2252, December.
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    Citations

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    Cited by:

    1. Horner, Mark & Downs, Joni, 2014. "Integrating people and place: A density-based measure for assessing accessibility to opportunities," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 1-18.
    2. Dalumpines, Ron & Scott, Darren M., 2017. "Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit," Journal of Transport Geography, Elsevier, vol. 59(C), pages 59-68.
    3. Lu, Wei & Scott, Darren M. & Dalumpines, Ron, 2018. "Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths," Journal of Transport Geography, Elsevier, vol. 71(C), pages 172-181.
    4. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    5. Yang, Wenyue & Chen, Huiling & Wang, Wulin, 2020. "The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    6. Scott, Darren M. & Lu, Wei & Brown, Matthew J., 2021. "Route choice of bike share users: Leveraging GPS data to derive choice sets," Journal of Transport Geography, Elsevier, vol. 90(C).
    7. Park, Yujin & Akar, Gulsah, 2019. "Why do bicyclists take detours? A multilevel regression model using smartphone GPS data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 191-200.

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