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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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    1. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    2. Zhu, Shanjiang & Levinson, David & Liu, Henry X. & Harder, Kathleen, 2010. "The traffic and behavioral effects of the I-35W Mississippi River bridge collapse," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 771-784, December.
    3. 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.
    4. Lei Zhang & David M. Levinson & Shanjiang Zhu, 2008. "Agent-Based Model of Price Competition, Capacity Choice, and Product Differentiation on Congested Networks," Journal of Transport Economics and Policy, University of Bath, vol. 42(3), pages 435-461, September.
    5. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    6. Cascetta, Ennio & Russo, Francesco & Viola, Francesco A. & Vitetta, Antonino, 2002. "A model of route perception in urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 577-592, August.
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    Cited by:

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    4. Bogyrbayeva, Aigerim & Kwon, Changhyun, 2021. "Pessimistic evasive flow capturing problems," European Journal of Operational Research, Elsevier, vol. 293(1), pages 133-148.
    5. 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.
    6. David Levinson & Hao Wu, 2020. "Towards a general theory of access," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    7. 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.
    8. Wenyun Tang & Lin Cheng, 2015. "Analyzing Multiday Route Choice Behavior using GPS Data," Working Papers 000135, University of Minnesota: Nexus Research Group.
    9. 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.
    10. Mengying Cui & David Levinson, 2021. "Shortest paths, travel costs, and traffic," Environment and Planning B, , vol. 48(4), pages 828-844, May.
    11. Alireza Ermagun & David M Levinson, 2019. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions," Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
    12. 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.
    13. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    14. 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.
    15. Fujino, Toru & Chen, Yu, 2020. "Effects of network structure on the performance of a modeled traffic network under drivers’ bounded rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    16. S. F. A. Batista & Ludovic Leclercq, 2019. "Regional Dynamic Traffic Assignment Framework for Macroscopic Fundamental Diagram Multi-regions Models," Transportation Science, INFORMS, vol. 53(6), pages 1563-1590, November.
    17. Ahmed El-Geneidy & David Levinson, 2011. "Place Rank: Valuing Spatial Interactions," Networks and Spatial Economics, Springer, vol. 11(4), pages 643-659, December.
    18. 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).
    19. 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.
    20. Hsueh, Chieh & Lin, Jen-Jia, 2023. "Influential factors of the route choices of scooter riders: A GPS-based data study," Journal of Transport Geography, Elsevier, vol. 113(C).
    21. 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.
    22. Manley, Ed & Cheng, Tao, 2018. "Exploring the role of spatial cognition in predicting urban traffic flow through agent-based modelling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 14-23.
    23. Carlos Carrion & David Levinson, 2019. "Overestimation and underestimation of travel time on commute trips: GPS vs. self- reporting," Working Papers 2019-05, University of Minnesota: Nexus Research Group.
    24. Xuan Di & Henry X. Liu & Shanjiang Zhu & David M. Levinson, 2017. "Indifference bands for boundedly rational route switching," Transportation, Springer, vol. 44(5), pages 1169-1194, September.

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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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