面向非常规突发事件驾驶行为的应急交通疏散微观仿真研究

The consequences of the unexpected events can be mitigated by effective and well organized emergency traffic evacuation management. Due to the complexity of the emergency evacuation problem, microscopic simulation-based traffic models are often applied to obtain a better understanding of the process of the evacuation at hand. Driving behavior during evacuation conditions has significant impacts on the evacuation time, evacuation routes, travel time reliability, traffic safety, and network performances. But to what extent do drivers change their driving behavior during evacuation conditions is still unknown, which leads to an inaccurate emergency traffic evacuation simulation model. The main reason of lacking quantitative analysis of driving behavior is that the unexpected event is unpredictable such that measuring the empirical data of driving behavior is nearly impossible. Driving simulators are used for research on human factors in order to monitor driver's behavior, performances, attentions and other characteristics in unexpected events. This project uses 8-degree freedom advanced driving simulator to investigate the changes in car-following and lane-changing behavior at emergency conditions, based on which new driving behavioral models, including car-following and lane-changing behaviors, are established for emergency conditions. Then an emergency traffic evacuation microscopic-traffic simulation model is built up on the basis of the newly established driving behavioral models. This ETE microscopic-traffic simulation model provides a realistic and efficient platform for researchers to investigate the traffic flow characteristics, network performances at emergency conditions and for traffic managers to determine efficient evacuation strategies in unexpected events.

应急交通疏散管理是应对非常规突发事件的重要措施，而驾驶行为模型是模拟应急交通疏散管理微观仿真的核心模块。非常规突发事件的突发性和高度不确定性等特点造成了目前的研究难以获取第一手驾驶行为数据，因此已有的非常规突发事件驾驶行为研究以定性分析或敏感性分析为主，缺乏一个定量模型，无法准确模拟面向应急交通疏散管理的微观仿真。针对以上问题，本项目综合采用8自由度和3维的高逼真度驾驶环境模拟、交通流微观仿真等实验方法，采集不同特性的驾驶员在非常规突发事件紧急状态下车辆跟驰与换道行为的相关数据，揭示驾驶行为反应规律，探索车辆跟驰和换道行为模型，为形成非常规突发事件驾驶行为模型提供基础，由此构建面向非常规突发事件驾驶行为的应急交通疏散微观仿真平台，为分析、评价和优化应急交通疏散管理提供实验平台和理论依据。研究成果将为采集非常规突发事件驾驶行为数据提供实验环境，为应急交通疏散管理的科学化提供理论支撑。

课题以雾霾团雾为典型非常规突发事件为研究对象，开展了非常规突发事件驾驶行为反应规律及其应急疏散微观交通仿真研究。主要成果包括：1）基于8自由度高逼真驾驶模拟器，搭建了的雾霾团雾天气下驾驶行为数据采集平台，为采集车辆跟驰行为数据提供了实验环境；2）采集了不同特性的驾驶员车辆跟驰 相关数据，分析其对驾驶行为的影响，包括车速及其变异性、加减速及其变异性、反应时间及其变异性、车头间距及其变异性等，解析了车辆跟驰行为机理，并建立了一个非常规突发事件微观驾驶行为模型；3）有机整合了驾驶模拟与微观交通仿真，结合驾驶模拟研究成果，构筑了面向非常规突发事件驾驶行为的应急交通疏散微观仿真实验平台，对面向非常规突发事件驾驶行为的应急交通疏散管理疏散路径、疏散时间、疏散交通安全、疏散路网性能等主要指标进行了分析、评价及其优化，并验证了模型的可靠性。研究成果将为采集非常规突发事件驾驶行为数据搭建了实验环境，为应急交通疏散管理的科学化提供了理论支撑。