城市轨道交通客流密集度指数研究

基于轨道交通物联网监测数据，从点、线、面三个层级，构建不同时间粒度车站、线路、网络的客流密集度指数计算模型和算法。车站客流密集度指数模型综合考虑影响车站密集度指数的关键区域（出入口、站台、楼扶梯、换乘通道）的拥挤程度和拥挤范围因素；线路的客流密集度指数模型综合考虑车站和区间的影响；网络的客流密集度指数模型由各线路的客流密集度指数加权得到。测试结果表明，提出的模型计算结果与实际地铁客流出行规律一致，可较好地反映地铁拥挤程度，为地铁客流运营拥挤状态评价和辅助决策提供技术支持。

The Research for Urban Rail Transit Passenger Crowd Index

This paper proposes a novel passenger crowd index computation model that utilizes the monitoring data of the urban track traffic network and is computed in three levels: station, line and network. The degree of crowdedness of key areas (such as entry, station platform, staircase and transfer channel) are considered in our computational model. Influence of station and interval is also taken into the consideration. the passenger flow crowd of the network is obtained by weighting the intensity of the lines. the experiments show that the proposed model is congruous with the traffic pattern of the subway transportation, reflects the commendable degree of crowdedness, and is efficient to provide technical support for state evaluation of and aid decision making for subway operation.