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Personalized Coordinated Routing with Utility Learning

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  • Ioannou, Petros
  • Wang, Zheyu

Abstract

Current navigation applications estimate travel times based on current traffic conditions, without considering upcoming travel demand. A potential solution to this problem is a central coordination system that collects global travel demand information, enabling more accurate traffic projections. Monetary incentives or charges can be used to encourage drivers to follow assigned routes that contribute toward a system optimum, which represents the most efficient traffic state for the transportation network. The challenge in determining incentives lies in the heterogeneity of drivers, as each has different preferences. Traditionally, surveys have been used to estimate drivers’ routing preferences, but these suffer from respondent inconsistency and changing driver preferences. This research proposes a utility learning mechanism. Instead of relying on static survey data, the system periodically updates its understanding of drivers’ preferences by observing their route choices over time. This research brief discusses the results and effectiveness of the coordinated routing system and the utility learning mechanism. View the NCST Project Webpage

Suggested Citation

  • Ioannou, Petros & Wang, Zheyu, 2026. "Personalized Coordinated Routing with Utility Learning," Institute of Transportation Studies, Working Paper Series qt37s3f9d4, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt37s3f9d4
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