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Learning Drivers’ Utility Functions in a Coordinated Freight Routing System Based on Drivers’ Actions

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  • Ioannou, Petros
  • Wang, Zheyu

Abstract

As urban areas grow and city populations expand, traffic congestion has become a significant problem, particularly in regions with substantial truck traffic. This study presents a coordinated freight routing system designed to optimize network utility and reduce congestion through personalized routing guidance and incentive mechanisms. The system customizes incentives and payments for individual drivers based on current traffic conditions and their specific routing preferences. Using a mixed logit model with a linear utility specification, the system captures drivers' route choice behaviors and decisions accurately. Participation is voluntary, ensuring most drivers receive a combined expected utility, including incentives, exceeding their anticipated utility under User Equilibrium (UE). This structure encourages drivers to follow suggested routes. Data collection on drivers' routing choices allows the system to update utility parameter estimates using a hierarchical Bayes estimator, ensuring routing suggestions remain relevant and effective. The system operates over defined intervals, where truck drivers submit their intended Origin-Destination (OD) pairs to a central coordinator. The coordinator assigns routes and payments, optimizing overall system costs and offering tailored incentives to maximize compliance. Experimental results on the Sioux Falls network validate the system's effectiveness, showing significant improvements in the objective function. This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior. View the NCST Project Webpage

Suggested Citation

  • Ioannou, Petros & Wang, Zheyu, 2024. "Learning Drivers’ Utility Functions in a Coordinated Freight Routing System Based on Drivers’ Actions," Institute of Transportation Studies, Working Paper Series qt6qb516n9, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt6qb516n9
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    More about this item

    Keywords

    Engineering; Social and Behavioral Sciences; Congestion management systems; Freight traffic; Incentives; Logits; Route choice; Routes and routing; Truck drivers;
    All these keywords.

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