Automating Traffic Model Enhancement with AI Research Agent

TL;DR

This paper introduces TR-Agent, an AI framework that autonomously develops and refines traffic models through iterative knowledge retrieval, hypothesis generation, and performance evaluation, significantly improving efficiency and robustness.

Contribution

The paper presents TR-Agent, a novel AI-powered research agent that automates traffic model development and refinement, incorporating a closed-loop pipeline with knowledge retrieval, hypothesis generation, and evaluation modules.

Findings

TR-Agent achieves substantial performance improvements on traffic models.

The framework demonstrates robustness across multiple real-world datasets.

TR-Agent provides interpretable explanations for model enhancements.

Abstract

Developing efficient traffic models is crucial for optimizing modern transportation systems. However, current modeling approaches remain labor-intensive and prone to human errors due to their dependence on manual workflows. These processes typically involve extensive literature reviews, formula tuning, and iterative testing, which often lead to inefficiencies. To address this, we propose TR-Agent, an AI-powered framework that autonomously develops and refines traffic models through a closed-loop, iterative process. We structure the research pipeline into four key stages: idea generation, theory formulation, theory evaluation, and iterative optimization, and implement TR-Agent with four corresponding modules. These modules collaborate to retrieve knowledge from external sources, generate novel hypotheses, implement and debug models, and evaluate their performance on evaluation datasets. Through iteratively feedback and refinement, TR-Agent improves both modeling efficiency and effectiveness. We validate the framework on three representative traffic models: the Intelligent Driver Model (IDM) for car-following behavior, the MOBIL model for lane-changing, and the Lighthill-Whitham-Richards (LWR) speed-density relationship for macroscopic traffic flow modeling. Experimental results show substantial performance gains over the original models. To assess the robustness and generalizability of the improvements, we conduct additional evaluations across multiple real-world datasets, demonstrating consistent performance gains beyond the original development data. Furthermore, TR-Agent produces interpretable explanations for each improvement, enabling researchers to easily verify and extend its results. This makes TR-Agent a valuable assistant for traffic modeling refinement and a promising tool for broader applications in transportation research.