# Route Recommendations for Traffic Management Under Learned Partial Driver Compliance

arXiv: https://arxiv.org/abs/2504.02993 · HTML: https://mlsanity.com/paper/2504.02993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [The SCIP Optimization Suite 9.0](https://mlsanity.com/paper/2402.17702/) (2024)
- [Expert with Clustering: Hierarchical Online Preference Learning Framework](https://mlsanity.com/paper/2401.15062/) (2024)
- [Congestion-Aware Routing, Rebalancing, and Charging Scheduling for Electric Autonomous Mobility-on-Demand System](https://mlsanity.com/paper/2110.04911/) (2021)
- [On Mobility Equity and the Promise of Emerging Transportation Systems](https://mlsanity.com/paper/2408.15401/) (2024)
- [Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic](https://mlsanity.com/paper/2003.04335/) (2020)
- [Socioeconomic Impact of Emerging Mobility Markets and Implementation Strategies](https://mlsanity.com/paper/2011.14399/) (2020)
- [Combined Optimal Routing and Coordination of Connected and Automated Vehicles](https://mlsanity.com/paper/2203.11408/) (2022)
- [Mechanism Design Theory in Control Engineering: A Tutorial and Overview of Applications in Communication, Power Grid, Transportation, and Security Systems](https://mlsanity.com/paper/2212.00756/) (2022)
- [Incentive Design for Eco-driving in Urban Transportation Networks](https://mlsanity.com/paper/2311.03682/) (2023)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment](https://mlsanity.com/paper/2505.20889/) (2025)
- [Cross-fitted Proximal Learning for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2604.05185/) (2026)

## Abstract (arXiv)

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.