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Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle

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  • Shanjiang Zhu
  • David Levinson

Abstract

Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. This study evaluates the widely applied shortest path assumption by evaluating routes followed by residents of the Minneapolis—St. Paul metropolitan area. Accurate Global Positioning System (GPS) and Geographic Information System (GIS) data were employed to reveal routes people used over an eight to thirteen week period. Most people did not choose the shortest path. Using three weeks of that data, we find that current route choice set generation algorithms do not reveal the majority of paths that individuals took. Findings from this study may guide future efforts in building better route choice models.

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  • Shanjiang Zhu & David Levinson, 2015. "Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0134322
    DOI: 10.1371/journal.pone.0134322
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    Cited by:

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    8. Bittihn, Stefan & Schadschneider, Andreas, 2021. "The effect of modern traffic information on Braess’ paradox," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    9. Albert Saiz & Luyao Wang, 2023. "Physical geography and traffic delays: Evidence from a major coastal city," Environment and Planning B, , vol. 50(1), pages 218-243, September.
    10. David Levinson & Hao Wu, 2020. "Towards a general theory of access," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    11. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Shortest path distance vs. least directional change: Empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, Elsevier, vol. 74(C), pages 37-52.
    12. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Objective vs. subjective measures of street environments in pedestrian route choice behaviour: Discrepancy and correlates of non-concordance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 1-23.
    13. Wenyun Tang & Lin Cheng, 2015. "Analyzing Multiday Route Choice Behavior using GPS Data," Working Papers 000135, University of Minnesota: Nexus Research Group.
    14. Shiguang Wang & Dexin Yu & Mei-Po Kwan & Huxing Zhou & Yongxing Li & Hongzhi Miao, 2019. "The Evolution and Growth Patterns of the Road Network in a Medium-Sized Developing City: A Historical Investigation of Changchun, China, from 1912 to 2017," Sustainability, MDPI, vol. 11(19), pages 1-25, September.
    15. Mengying Cui & David Levinson, 2021. "Shortest paths, travel costs, and traffic," Environment and Planning B, , vol. 48(4), pages 828-844, May.
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    18. Longsheng Sun & Mark H. Karwan & Changhyun Kwon, 2018. "Generalized Bounded Rationality and Robust Multicommodity Network Design," Operations Research, INFORMS, vol. 66(1), pages 42-57, 1-2.
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    20. 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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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles

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