Recovering Scheduling Preferences in Dynamic Departure Time Models

André de Palma, Zhenyu Yang, Pietro Giardina and Nikolas Gerolimnis ()
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Nikolas Gerolimnis: CY Cergy Paris Université, THEMA

No 2025-15, THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise

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.

Keywords: Bottleneck, Scheduling preferences, Traffic flow; Travel demand management (search for similar items in EconPapers)
JEL-codes: C25 D12 R41 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-tre
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