Route Recommendations for Traffic Management Under Learned Partial Driver Compliance
This work addresses traffic management for urban planners by incorporating learned driver behavior, though it is incremental as it builds on existing optimization methods with a new compliance model.
The paper tackles traffic congestion by guiding drivers on system-optimal routes, but addresses the issue of imperfect driver compliance by learning it from historical data and optimizing flow under realistic adherence; simulations on a grid network show significant travel time reductions compared to baselines.
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.