AI 中文总结
该研究针对路网养护规划中反复求解均衡交通流计算成本过高的问题，提出以优化均衡解为真值的数据驱动代理模型，经纽瓦克交通数据验证，可作为可扩展构件用于养护调度框架。
AI 中文摘要
路网级养护规划需在道路通行能力下降时反复评估均衡交通流。尽管均衡交通分配模型已成熟，但反复求解会带来极高计算成本，难以嵌入养护调度问题。本文研究数据驱动的代理模型，以基于优化的均衡解为真值，直接从起讫点需求近似均衡路段流量。基于新泽西州纽瓦克市交通数据的实际案例研究表明，该方法作为可扩展构件，适用于未来养护调度框架，具备有效性。
英文摘要
Network-level maintenance planning requires repeated evaluations of equilibrium traffic flows under road capacity reductions. While equilibrium traffic assignment models are well established, their repeated solution quickly becomes computationally prohibitive and challenging to embed within maintenance scheduling problems. This paper investigates data-driven surrogate models that approximate equilibrium arc flows directly from origin-destination demand, using optimization-based equilibrium solutions as ground truth. A real-world case study based on traffic data from the Newark, New Jersey area demonstrates the effectiveness of the proposed approach as a scalable building block for future maintenance scheduling frameworks.
CommentsCase Study paper presented in IISE Annual Conference and Expo 2026
