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A model of two-destination choice in trip chains with GPS data

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  • Huang, Arthur
  • Levinson, David

Abstract

Studying trip chaining behavior has been a challenging endeavor which requires the support of microscopic travel data. New insights into trip chaining can be gained from real-time GPS travel data. This research introduces a framework that considers two-destination choice in the context of home-based trip chains. We propose and empirically compare three alternatives of building choice sets where we consider various relationships of the two destinations (such as major–minor destinations, selecting one first, and selecting two concurrently). Our choice set formation alternatives use survival models to determine the selection probability of a destination. Our results reveal that trip chaining behavior is shaped by the features of retail clusters, spatial patterns of clusters, transportation networks, and the axis of travel. This research reveals that not only the spatial relationship but also the land use relationship of the destinations in a trip chain affect the decision making process.

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  • Huang, Arthur & Levinson, David, 2017. "A model of two-destination choice in trip chains with GPS data," Journal of choice modelling, Elsevier, vol. 24(C), pages 51-62.
    • Handle: RePEc:eee:eejocm:v:24:y:2017:i:c:p:51-62
    DOI: 10.1016/j.jocm.2016.04.002
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    2. Pizzol, Bruna & Strambi, Orlando & Giannotti, Mariana & Arbex, Renato Oliveira & Alves, Bianca Bianchi, 2021. "Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones," Journal of choice modelling, Elsevier, vol. 39(C).
    3. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    4. Zong, Fang & Tian, Yongda & He, Yanan & Tang, Jinjun & Lv, Jianyu, 2019. "Trip destination prediction based on multi-day GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 258-269.
    5. Yuan Yuan & Chunfu Shao & Zhichao Cao & Chaoying Yin, 2023. "The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model," IJERPH, MDPI, vol. 20(5), pages 1-29, March.
    6. Emre Avuçlu & Fatih Başçiftçi, 2018. "A new approach to minimize the loss of life after natural hazards by using Android operating system," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(2), pages 1005-1016, January.

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    More about this item

    Keywords

    GPS data; Trip chaining; Axis of travel; Destination choice;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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