大型活动期间平衡出行效率与环境暴露的多智能体路径框架
原标题：Balancing Mobility Efficiency and Environmental Exposure During Mega Events: A Multi-agent Routing Framework
AI 摘要
该研究提出一个暴露感知的多智能体路径规划框架，在统一仿真环境中整合路网状况、活动引发的需求、背景交通与环境数据，让机动车出行者优化效率、微出行者兼顾时间与动态PM2.5和噪声暴露。与最短距离、拥堵感知和暴露感知基线相比，基于SARSA的框架在高需求下将车辆出行时间降低34.59%、累计环境暴露降低58.65%，而微出行时间基本不变。该框架可为车辆分流、低暴露路径引导和健康导向的交通管理提供量化支持。
正文节选
Balancing Mobility Efficiency and Environmental Exposure During Mega Events: A Multi-agent Routing Framework Abstract Mega events can generate concentrated travel demand, intensifying congestion around venues and increasing travelers’ environmental exposure. Existing traffic management strategies focus on travel efficiency, with limited attention to exposure risks. Taking mega-event travel as a short-term high-demand scenario, this study develops an exposure-aware multi-agent routing framework i