HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-World 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, August 2026). 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 deployments. HLSR addresses this by fusing 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 in multi-cost route allocation. The approach targets urban traffic congestion, which reduces productivity and increases travel cost and emissions, by rerouting only the vehicles that need guidance rather than the entire network.

Paper Overview

Research Area: 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.

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

Selective intervention: reroutes only affected vehicles instead of replanning the entire network every decision period
Dual-threshold congestion detection with calibrated upstream selection
Driver-tailored travel-time prediction
Approaching-vehicle expansion to include vehicles heading toward congestion
Travel-time-weighted k-shortest-path generation
Horizon-dependent hybrid live-forecast segment speeds for multi-cost route allocation