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A Dynamic Discrete Choice Activity-Based Travel Demand Model

Author

Listed:
  • Oskar Blom Västberg

    (Division of Systems Analysis and Economics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden;)

  • Anders Karlström

    (Division of Systems Analysis and Economics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden;)

  • Daniel Jonsson

    (Division of Systems Analysis and Economics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden;)

  • Marcus Sundberg

    (Division of Systems Analysis and Economics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

Abstract

This paper presents a dynamic discrete choice model (DDCM) for daily activity–travel planning. A daily activity–travel pattern is constructed from a sequence of decisions of when, where, why, and how to travel. Individuals’ preferences for activity–travel patterns are described by the sum of the utility of all travel and activity episodes in that pattern, but components of the utility functions, such as travel times, may be stochastic. In each decision stage, individuals act as if they maximized the expected utility of the remainder of the day. The DDCM-model presented allows for a detailed treatment of timing decision consistent with other choice dimensions, respects time–space constraints, and enables the inclusion of explicitly modeled uncertainties in, for example, travel time. In a case study, a model for daily planning of activity and travel on workdays is estimated whereby individuals can perform any number of trips that each is a combination of one of 1,240 locations, four modes, and six activities. Simulation results indicate that the model within sample accurately replicates timing decisions, trip lengths, and the distributions of the number of trips, tours, and trips per tour.

Suggested Citation

  • Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    • Handle: RePEc:inm:ortrsc:v:54:y:2020:i:1:p:21-41
    DOI: 10.1287/trsc.2019.0898
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    References listed on IDEAS

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    Cited by:

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    2. Kai Shen & Jan-Dirk Schmöcker & Wenzhe Sun & Ali Gul Qureshi, 2023. "Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward," Transportation, Springer, vol. 50(5), pages 1897-1921, October.
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    4. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.

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