IncidentResponseGPT：利用生成式人工智能生成交通事件响应方案
IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence
AI总结：
本文提出 IncidentResponseGPT 框架，利用生成式 AI 生成区域自适应的交通事件响应方案，并结合 TOPSIS 方法对方案进行排序，以加速决策并降低事件对城市交通网络的影响。
摘要：
本文提出的 IncidentResponseGPT 框架是一个新颖的系统，它应用生成式人工智能（AI）来潜在地提升交通事件响应的效率和有效性。该模型能够综合特定区域的交通事件响应指南，并生成适应特定区域的交通事件响应方案，旨在加快交通管理部门的决策速度。该方法旨在通过提出各种建议（例如最优改道策略、估算资源需求）来缩短事件解决时间，从而最大限度地减少对城市交通网络的整体影响。系统会建议具体行动，包括动态车道封闭、优化改道以及调度适当的应急资源。我们利用基于与理想解相似度的排序偏好技术（TOPSIS），根据影响最小化和资源效率等准则，并基于各方案与人工提出方案的接近程度，对生成的响应方案进行排序。
英文摘要：
The proposed IncidentResponseGPT framework - a novel system that applies generative artificial intelligence (AI) to potentially enhance the efficiency and effectiveness of traffic incident response. This model allows for synthesis of region-specific incident response guidelines and generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities. This approach aims to accelerate incident resolution times by suggesting various recommendations (e.g. optimal rerouting strategies, estimating resource needs) to minimize the overall impact on the urban traffic network. The system suggests specific actions, including dynamic lane closures, optimized rerouting and dispatching appropriate emergency resources. We utilize the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank generated response plans based on criteria like impact minimization and resource efficiency based on their proximity to an human-proposed solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
