大规模人群网络流瓶颈识别方法研究

在体育赛事、集会等场合,大规模人群沿路网流动过程中易发生拥塞并诱发恐慌、踩踏等严重事故.因此,理解网络结构对人群流动特征的影响,对大规模人群在网络上的流动进行有效的组织和管理十分必要.首先结合大型活动行人路径选择行为的一般特征,采用交通分配模型将大规模人群分配到具体的网络拓扑结构上,采用非线性行人路阻函数模拟人群拥挤效应;随后,计算路网流量增加条件下人群在各路段走行时间变化率,并提出以路段走行时间变化率排序作为各路段结构重要度排序的方法,据此计算各路段的鲁棒性,确定瓶颈路段.最后,以德国2010年"爱的大游行"踩踏事故为例,分析验证了大规模人群网络流瓶颈识别方法,研究讨论了对路网实施管控的流量阈值以及不同管控条件下网络流的定量演化规律,可为路网的科学管理和规划提供基础数据和理论依据.

Method of bottleneck identification for massive crowd circulating network

Under scenario of sports events and other kinds of massive gathering, a large amount of people would travel along road networks, thus forming crowded pedestrians. The high density situation might induce in mass panic and stampede, causing serious injuries and casualties. Therefore, it is necessary to firstly understand pedestrian flow features on the corresponding network and then apply proper control method to organize and manage pedestrian flow. Firstly, considering general characteristics of travelers' route choice behavior, all these pedestrians are dynamically assigned onto the road network via a traffic assignment model. Secondly, a nonlinear pedestrian road impedance function is introduced to address the congestion effects of massive crowds, and the pedestrian travel time changing rate is calculated for each link under various traffic volumes. It is proposed to sort the networks links according to the travel time changing rate. The rank is thus defined as a measurement of the structural importance of each link. Further, the structural importance is used to determine link robustness under different pedestrian traffic demand. Combing the result of structural importance and link robustness, bottleneck of the crowd circulating network can be identified. Finally, the Love Parade 2010 in Duisburg (an electronic music festival) is adopted as a case to illustrate the applicability of the proposed network bottleneck identification method. The threshold of pedestrian volume on the network and the effect of road control strategy are further investigated, the obtained basic data and theoretical results can benefit scientific planning and management of massive crowd road traffic.