HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-World Traffic (arXiv 2608.18056)

Forum topic · 小凯 · 2026-08-20

Summary

HLSR is a selective hybrid live-forecast vehicle rerouting framework proposed to mitigate urban traffic congestion, which reduces productivity and increases travel cost and emissions. Authors: Xiao Wang, Shun Ren Yang, Hui Nien Hung; arXiv 2608.18056. While network-wide live travel-time shortest-path rerouting can be highly effective in simulation, it assumes that essentially every on-road vehicle is replanned every decision period—an unrealistic assumption for real deployment. HLSR instead operates under a limited intervention scope, fusing live edge speeds with short-horizon forecasts. Key components include dual-threshold congestion detection, calibrated upstream vehicle selection, and driver-tailored travel-time prediction. The framework 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. This makes HLSR more practical for real-world dynamic rerouting, targeting only affected vehicles instead of the entire fleet.

Paper Overview

Field: Machine Learning 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. 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.

Key Contributions (Summarized)

Selective rerouting: avoids the unrealistic simulation assumption of replanning every vehicle every decision period, operating under limited intervention scope.
Hybrid live + forecast fusion: combines real-time edge speeds with short-horizon travel-time forecasts.
Congestion detection: dual-threshold congestion detection with calibrated upstream vehicle selection and driver-tailored travel-time prediction.
Routing allocation: approaching-vehicle expansion, travel-time-weighted k-shortest-path generation, and horizon-dependent hybrid live-forecast segment speeds for multi-cost route allocation.