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Penalty-based strategies, such as congestion pricing, have been employed to improve traffic network efficiency, but they face criticism for their negative impact on users and equity concerns. Collaborative routing, which allows users to negotiate route choices, offers a solution that considers individual heterogeneity. Personalized incentives can encourage such collaboration and are more politically acceptable than penalties. This study proposes a collaborative routing strategy that uses personalized incentives to guide users towards desired traffic states while promoting multidimensional equity. Three equity dimensions are considered: accessibility equity (equal access to jobs, services, and education), inclusion equity (route suggestions and incentives that do not favor specific users), and utility equity (envy-free solutions where no user feels others have more valuable incentives). The strategy prioritizes equitable access to societal services and activities, ensuring accessibility equity in routing solutions. Inclusion equity is maintained through non-negative incentives that consider user heterogeneity without excluding anyone. An envy-free compensation mechanism achieves utility equity by eliminating envy over incentive-route bundles. A constrained traffic assignment (CTA) formulation and consensus optimization variant are then devised to break down the centralized problem into smaller, manageable parts and a decentralized algorithm is developed for scalability in large transportation networks and user populations. Numerical studies investigate the model's enhancement of equity dimensions and the impact of hyperparameters on system objective tradeoffs and demonstrate the algorithm convergence.
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Personalized Freight Route Recommendations with System Optimality Considerations: A Utility Learning Approach
Traffic congestion has a negative economic and environmental impact. Traffic conditions become even worse in areas with high volume of trucks. In this paper, we propose a coordinated pricing-and-routing scheme for truck drivers to efficiently route trucks into the network and improve the overall traffic conditions. A basic characteristic of our approach is the fact that we provide personalized routing instructions based on drivers’ individual routing preferences. In contrast with previous works that provide personalized routing suggestions, our approach optimizes over a total system-wide cost through a combined pricing-and-routing scheme that satisfies the budget balance on average property and ensures that every truck driver has an incentive to participate in the proposed mechanism by guaranteeing that the expected total utility of a truck driver (including payments) in case he/she decides to participate in the mechanism, is greater than or equal to his/her expected utility in case he/she does not participate. Since estimating a utility function for each individual truck driver is computationally intensive, we first divide the truck drivers into disjoint clusters based on their responses to a small number of binary route choice questions and we subsequently propose to use a learning scheme based on the Maximum Likelihood Estimation (MLE) principle that allows us to learn the parameters of the utility function that describes each cluster. The estimated utilities are then used to calculate a pricing-and-routing scheme with the aforementioned characteristics. Simulation results in the Sioux Falls network demonstrate the efficiency of the proposed pricing-and-routing scheme.
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- Award ID(s):
- 1932615
- PAR ID:
- 10455967
- Date Published:
- Journal Name:
- IEEE transactions on intelligent transportation systems
- ISSN:
- 1524-9050
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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