Route Recommendations for Traffic Management Under Learned Partial Driver Compliance

2025-04-03

AI Summary

This paper addresses the problem of ineffective congestion mitigation in traffic management due to drivers’ incomplete compliance with route recommendations. To tackle this, we propose a dynamic route recommendation framework that jointly integrates learning-based compliance modeling and system-optimal flow optimization. Our key innovation is the first explicit incorporation of personalized compliance rates—estimated from historical driving behavior—into traffic control, thereby relaxing the conventional assumption of full compliance. We further formulate a robust recommendation mechanism grounded in stochastic optimization to explicitly model heterogeneous user responsiveness under uncertainty. Evaluated on grid-network simulations, our approach significantly reduces average travel time, outperforming benchmark strategies by a substantial margin. Results demonstrate both the efficacy and practicality of learning and leveraging driver compliance patterns for real-world traffic system optimization.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we recognize that most theoretical approaches assume perfect driver compliance, which often does not reflect reality, as drivers tend to deviate from recommendations to fulfill their personal objectives. Therefore, we propose a route recommendation framework that explicitly learns partial driver compliance and optimizes traffic flow under realistic adherence. We first compute an SO edge flow through flow optimization techniques. Next, we train a compliance model based on historical driver decisions to capture individual responses to our recommendations. Finally, we formulate a stochastic optimization problem that minimizes the gap between the target SO flow and the realized flow under conditions of imperfect adherence. Our simulations conducted on a grid network reveal that our approach significantly reduces travel time compared to baseline strategies, demonstrating the practical advantage of incorporating learned compliance into traffic management.

Problem

• Mitigate congestion by guiding travelers on optimal routes
• Address partial driver compliance with route recommendations
• Optimize traffic flow under realistic driver adherence

Innovation

• Optimizes traffic flow with partial compliance
• Learns driver compliance from historical decisions
• Minimizes gap between target and realized flow