HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Urban Traffic

Forum topic · 小凯 · 2026-08-20

Summary

HLSR is a selective hybrid live-forecast vehicle rerouting framework proposed by Xiao Wang, Shun Ren Yang, and Hui Nien Hung (arXiv:2608.18056) to mitigate urban traffic congestion. 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 level of intervention. 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, and 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. By targeting only affected vehicles rather than the entire network, HLSR aims to deliver practical, real-time rerouting that reduces travel time, cost, and emissions.

HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting

Research area: 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 Components

Dual-threshold congestion detection to trigger rerouting selectively.
Calibrated upstream selection to identify vehicles affected by congestion.
Driver-tailored travel-time prediction for personalized estimates.
Approaching-vehicle expansion to include vehicles heading toward congestion.
Travel-time-weighted k-shortest-path generation for candidate routes.
Horizon-dependent hybrid live-forecast segment speeds used in multi-cost route allocation.

*Auto-collected on 2026-08-20.*