用数据模型替代复杂交通仿真，快速预测道路维修对车流影响。
Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows
- 构建基于优化求解的代理模型，直接由客流需求预测交通流量。
- 在纽瓦克真实路网中验证，计算效率提升显著且精度可靠。
- 适合交通规划者在大规模维修调度中快速评估路况影响。
网络级道路维护规划需要反复评估道路容量降低下的均衡交通流。尽管均衡交通分配模型已成熟，但其重复求解迅速变得计算成本高昂，难以嵌入维护调度问题。本文研究了数据驱动的代理模型，直接从起讫点需求预测均衡弧流量，以优化求解结果作为真实标签。基于新泽西州纽瓦克地区的实际交通数据开展案例研究，证明该方法作为未来维护调度框架中可扩展组件的有效性。
原文摘要 · Abstract (English)
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.
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