Automating the loop in traffic incident management on highway

2025-03-15

AI Summary

Highway traffic incident response suffers from significant delays and heavy reliance on manual decision-making. Method: This paper proposes a closed-loop decision support system that deeply integrates large language models (LLMs) with operations research optimization. We introduce a novel “LLM + Optimization” hybrid paradigm: the LLM handles natural language understanding, contextual reasoning, and human–AI interaction, while the optimization module ensures mathematical rigor and real-time feasibility of dispatch solutions; additionally, we design a fully LLM-driven autonomous decision pathway to extend AI applicability to high-risk, time-critical scheduling scenarios. Contribution/Results: Evaluated on real-world incident data from Autostrade per l’Italia, the system significantly outperforms pure-LLM baselines in decision consistency, accuracy, and robustness—meeting stringent requirements for safety-critical deployment. It enhances emergency response safety, timeliness, and traffic throughput efficiency.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Effective traffic incident management is essential for ensuring safety, minimizing congestion, and reducing response times in emergency situations. Traditional highway incident management relies heavily on radio room operators, who must make rapid, informed decisions in high-stakes environments. This paper proposes an innovative solution to support and enhance these decisions by integrating Large Language Models (LLMs) into a decision-support system for traffic incident management. We introduce two approaches: (1) an LLM + Optimization hybrid that leverages both the flexibility of natural language interaction and the robustness of optimization techniques, and (2) a Full LLM approach that autonomously generates decisions using only LLM capabilities. We tested our solutions using historical event data from Autostrade per l'Italia. Experimental results indicate that while both approaches show promise, the LLM + Optimization solution demonstrates superior reliability, making it particularly suited to critical applications where consistency and accuracy are paramount. This research highlights the potential for LLMs to transform highway incident management by enabling accessible, data-driven decision-making support.

Problem

• Automating traffic incident management on highways
• Enhancing decision-making with Large Language Models
• Improving safety and reducing congestion in emergencies

Innovation

• Integrates LLMs for traffic incident decisions
• Combines LLM with optimization for reliability
• Autonomously generates decisions using LLMs