HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting Framework

Summary

HLSR is a selective hybrid live-forecast vehicle rerouting framework designed to mitigate urban traffic congestion, which reduces productivity and increases travel costs and emissions. While network-wide live travel-time shortest-path rerouting can be highly effective in simulation, it assumes that nearly every on-road vehicle is replanned every decision period—an unrealistic assumption in practice. HLSR instead fuses live edge speeds with short-horizon forecasts under a limited intervention scope. The framework builds on dual-threshold congestion detection, calibrated upstream selection, and driver-tailored travel-time prediction. It further introduces approaching-vehicle expansion, travel-time-weighted k-shortest-path generation, and a horizon-dependent hybrid live-forecast segment speed used for multi-cost route allocation. The paper (arXiv:2608.18056) by Xiao Wang, Shun Ren Yang, and Hui Nien Hung falls within the machine learning domain and was published on August 18, 2026.