提出一种智能选路系统，实时避开拥堵，只改道少量车辆却效果显著。
HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance
- 只重规划部分车辆，结合实时速度与短期预测
- 在有限干预下降低平均通勤时间18.7%
- 适合城市交通管理、智慧出行平台使用
城市交通拥堵降低生产效率并增加出行成本与排放。全局实时最短路径重规划在模拟中有效，但需每周期重算几乎所有在路上的车辆。我们提出HLSR，一种选择性混合实时-预测车辆路径重规划框架，融合实时路段速度与短时预测，在干预范围受限条件下运行。基于双阈值拥堵检测、校准上游车辆选择和驾驶员定制化行程时间预测，HLSR进一步引入接近车辆扩展、行程时间加权k最短路径生成，以及多成本路径分配中依赖时域的混合实时-预测段速计算。
原文摘要 · Abstract (English)
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.
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