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

Forum topic · 小凯 · 2026-08-20

Summary

Researchers Xiao Wang, Shun Ren Yang, and Hui Nien Hung propose HLSR, a selective hybrid live-forecast vehicle rerouting framework presented in arXiv paper 2608.18056 (August 2026). Urban congestion reduces productivity and raises travel costs and emissions; network-wide live travel-time shortest-path rerouting works well in simulation but unrealistically assumes nearly every vehicle is replanned every decision period. HLSR addresses this by fusing 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 in multi-cost route allocation, making rerouting practical for real deployments.

Paper Overview

Field: Machine Learning
Authors: Xiao Wang, Shun Ren Yang, Hui Nien Hung
Published: 2026-08-18
arXiv: 2608.18056

Original 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 Ideas

Selective rerouting: avoids the unrealistic simulation assumption that every vehicle is replanned each decision period, operating instead under limited intervention scope.
Hybrid live-forecast fusion: combines real-time edge speeds with short-horizon traffic forecasts.
Core mechanisms:
Dual-threshold congestion detection
Calibrated upstream vehicle selection
Driver-tailored travel-time prediction
Approaching-vehicle expansion
Travel-time-weighted k-shortest-path generation
Horizon-dependent hybrid live-forecast segment speeds for multi-cost route allocation

Significance

HLSR aims to bridge the gap between simulation-effective rerouting and real-world deployability by intelligently choosing which vehicles to reroute and how to blend live and forecasted speed data.