Developing efficient traffic models is essential for optimizing transportation systems, yet current approaches remain time-intensive and susceptible to human errors due to their reliance on manual processes. Traditional workflows involve exhaustive literature reviews, formula optimization, and iterative testing, leading to inefficiencies in research. In response, we introduce the Traffic Research Agent (TR-Agent), an AI-driven system designed to autonomously develop and refine traffic models through an iterative, closed-loop process. Specifically, we divide the research pipeline into four key stages: idea generation, theory formulation, theory evaluation, and iterative optimization; and construct TR-Agent with four corresponding modules: Idea Generator, Code Generator, Evaluator, and Analyzer. Working in synergy, these modules retrieve knowledge from external resources, generate novel ideas, implement and debug models, and finally assess them on the evaluation datasets. Furthermore, the system continuously refines these models based on iterative feedback, enhancing research efficiency and model performance. Experimental results demonstrate that TR-Agent achieves significant performance improvements across multiple traffic models, including the Intelligent Driver Model (IDM) for car following, the MOBIL lane-changing model, and the Lighthill-Whitham-Richards (LWR) traffic flow model. Additionally, TR-Agent provides detailed explanations for its optimizations, allowing researchers to verify and build upon its improvements easily. This flexibility makes the framework a powerful tool for researchers in transportation and beyond. To further support research and collaboration, we have open-sourced both the code and data used in our experiments, facilitating broader access and enabling continued advancements in the field.

翻译：开发高效的交通模型对于优化交通系统至关重要，然而当前方法因其对人工流程的依赖，仍然耗时且易受人为错误影响。传统工作流程涉及详尽的文献综述、公式优化和迭代测试，导致研究效率低下。为此，我们引入了交通研究代理（TR-Agent），这是一个通过迭代闭环过程自主开发和优化交通模型的AI驱动系统。具体而言，我们将研究流程划分为四个关键阶段：想法生成、理论构建、理论评估和迭代优化；并构建了包含四个对应模块的TR-Agent：想法生成器、代码生成器、评估器和分析器。这些模块协同工作，从外部资源获取知识，生成新想法，实现并调试模型，最后在评估数据集上对其进行评估。此外，系统基于迭代反馈持续优化这些模型，从而提升研究效率和模型性能。实验结果表明，TR-Agent在多个交通模型上实现了显著的性能提升，包括用于跟驰的智能驾驶员模型（IDM）、MOBIL换道模型以及Lighthill-Whitham-Richards（LWR）交通流模型。此外，TR-Agent为其优化提供了详细解释，使研究人员能够轻松验证并基于其改进进行构建。这种灵活性使该框架成为交通及其他领域研究人员的强大工具。为了进一步支持研究和协作，我们已开源实验中使用的代码和数据，以促进更广泛的访问并推动该领域的持续进步。