学习司机部分服从行为，优化真实路况下的路径推荐。
Route Recommendations for Traffic Management Under Learned Partial Driver Compliance
- 基于历史数据训练司机服从模型，捕捉真实出行决策
- 在不完美服从条件下最小化实际流量与最优流量的差距
- 仿真显示旅行时间显著降低，适合智能交通系统应用
本文旨在通过引导旅客选择系统最优（SO）路径来缓解交通拥堵。然而，现有理论多假设司机完全服从推荐，这与现实不符，因司机常为个人目标偏离建议路线。为此，我们提出一种显式学习部分司机服从行为的路径推荐框架，并在现实遵从条件下优化交通流。首先，通过流量优化技术计算出系统最优边流量；其次，基于历史司机决策训练服从模型，捕捉个体对推荐的反应；最后，构建随机优化问题，最小化在不完全服从条件下的目标SO流量与实际流量之间的差距。在网格网络上的仿真结果表明，该方法相比基线策略显著降低了旅行时间，验证了将学习到的服从行为融入交通管理的实际优势。
原文摘要 · Abstract (English)
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.
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