发表机构
National Tsing Hua University; National Chiao Tung University（国立清华大学; 国立交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市交通拥堵问题，提出HLSR框架，通过融合实时与预测数据的选择性重路由方法，实现实时拥堵规避，提升重路由的有效性。
AI 中文摘要
城市交通拥堵会降低生产力、增加出行成本与碳排放。网络范围的实时出行时间最短路径重路由在模拟中效果显著，但该方法假设每一个上路车辆都要在每个决策周期重新规划路线。本文提出HLSR，一种选择性混合实时-预测车辆重路由框架，在有限干预范围内融合实时边缘速度与短时间范围预测。该框架基于双阈值拥堵检测、校准的上游选择及适配驾驶员的出行时间预测构建，还引入接近车辆扩展、出行时间加权的k最短路径生成，以及依赖时间范围的混合实时-预测路段速度，用于多成本路线分配。
英文摘要
Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be highly effective in simulation, but assumes that essentially every on-road vehicle is replanned every decision period. We propose HLSR, a selective hybrid live--forecast vehicle rerouting framework that fuses live edge speeds with short-horizon forecasts under limited intervention scope. Building on dual-threshold congestion detection, calibrated upstream selection, and driver-tailored travel-time prediction, HLSR further introduces approaching-vehicle expansion, travel-time-weighted k-shortest-path generation, and a horizon-dependent hybrid live--forecast segment speed used in multi-cost route allocation.
