HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance· HLSR：混合实时预测选择性动态车辆重路由
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.
提出HLSR，一种混合实时预测选择性动态车辆重路由系统，用于缓解城市交通拥堵。
- 核心方法
- 利用双重阈值拥堵检测、上游选择校准、驾驶员定制的出行时间预测，HLSR引入接近车辆扩展、加权最短路径生成和时间范围依赖的混合实时预测路段速度来分配多成本路线。
- 适合谁读
- 研究者、工程师、城市规划者
- 要解决的问题
- 城市交通拥堵导致生产率下降、出行成本和排放增加，现有重路由方法假设每辆车都需要在每个决策周期被重新规划，难以实现实时拥堵避免。
- 关键实验
- 未提供
- 主要贡献
- 1. 提出了一种选择性混合实时预测车辆重路由框架；2. 实现了有限干预下的有效性和实时性；3. 融合了实时速度与短期预测，提高了路线规划的准确性。
- 意义与局限
- HLSR框架在缓解城市交通拥堵方面展示了潜在的有效性和可实施性，但其实际影响和局限性需要通过进一步的实验验证，未来可应用于智能交通系统和城市规划。