Summary
HLSR is a selective hybrid live-forecast vehicle rerouting framework proposed to reduce urban traffic congestion without replanning every vehicle each decision period. Unlike network-wide live travel-time shortest-path rerouting, which assumes essentially all on-road vehicles are rerouted every cycle, HLSR fuses live edge speeds with short-horizon forecasts under a limited intervention scope. The framework combines 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 is authored by Xiao Wang, Shun Ren Yang, and Hui Nien Hung, available on arXiv as 2608.18056.
Paper Overview
Field: ML
Authors: Xiao Wang, Shun Ren Yang, Hui Nien Hung
Published: 2026-08-18
arXiv: 2608.18056
Abstract
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. The authors 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
- A horizon-dependent hybrid live-forecast segment speed used in multi-cost route allocation
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*Auto-collected on 2026-08-20*
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