Crash Scene to Resolution: LLM-based Agents Driven for Efficient Traffic Accident Handling

Generative agents, capable of simulating human behavior and collaborating on complex tasks, have the potential to revolutionize the investigation of traffic accidents. In this study, we designed TAA (Traffic Accident Agents), an advanced framework based on extended large language models (LLMs) to digitally model urban accident handling procedures and stakeholder interactions. TAA formalizes the roles, responsibilities, and interactions of all stakeholders through natural language encoding, utilizing this knowledge base to orchestrate its execution workflows. Facilitates trusted agent interactions, generates comprehensive reports, and employs memory mechanisms to plan and optimize subsequent actions. We evaluated TAA performance across multiple versions of ChatGPT, focusing on its capabilities to generate reliable interactions, make context-sensitive decisions, maintain extended dialogues, and produce accurate reports in streamlined accident resolution scenarios. Our analysis included evaluations of token consumption and economic costs to ensure scalability and practicality, with TAA achieving 87.9% effectiveness on the GPT 4omini benchmark. Experimental results demonstrate TAA’s successful execution of urban accident handling workflows with maintained informational consistency. The framework shows broad applicability to accident investigation, reconstruction, and archival documentation. This work pioneers the use of generative agents as collaborative human proxies, offering a transformative pathway to advance the future of traffic accident management and investigation.