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Recovering Scheduling Preferences in Dynamic Departure Time Models

Author

Listed:
  • André de Palma
  • Zhenyu Yang
  • Pietro Giardina
  • Nikolas Gerolimnis

    (CY Cergy Paris Université, THEMA)

Abstract

We aim to infer commuters’ scheduling preferences from their observed arrival times, given an exogenous traffic congestion pattern. To do this, we employ a structural model that characterizes how users balance congestion costs against the penalties for arriving early or late relative to an ideal time. In this framework, each commuter selects an arrival time that minimizes her overall trip cost by considering the within-day congestion pattern along with her individual scheduling preference. By incorporating the distribution of these preferences and desired arrival times across the population, we can estimate the likelihood of observing arrivals at specific times. Using synthetic data, we then apply the maximum likelihood estimation (MLE) method to recover the parameters of the joint distribution of scheduling preferences and desired arrival times. Our numerical results demonstrate the effectiveness of the proposed method.

Suggested Citation

  • André de Palma & Zhenyu Yang & Pietro Giardina & Nikolas Gerolimnis, 2025. "Recovering Scheduling Preferences in Dynamic Departure Time Models," THEMA Working Papers 2025-15, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    • Handle: RePEc:ema:worpap:2025-15
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    References listed on IDEAS

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    1. Lucas javaudin & André de Palma, 2024. "METROPOLIS2: Bridging Theory and Simulation in Agent-Based Transport Modeling," THEMA Working Papers 2024-03, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    2. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    3. AndrÊ de Palma & Robin Lindsey, 2000. "Private toll roads: Competition under various ownership regimes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 34(1), pages 13-35.
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    6. Andre de Palma & Moshe Ben-Akiva & Claude Lefevre & Nicolaos Litinas, 1983. "Stochastic Equilibrium Model of Peak Period Traffic Congestion," Transportation Science, INFORMS, vol. 17(4), pages 430-453, November.
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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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