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城市轨道交通乘客交通特性分析及建模
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摘要
在采集城市轨道交通乘客进站、出站、换乘、候乘等交通特性数据基础上,从乘客个体和客流群体的角度,分别研究了乘客的微观特性和宏观特性,并建立了相应的模型。
     在乘客微观交通特性分析方面,采用数据挖掘的方法,探寻城市轨道交通乘客个体在不同坡度的通道内及楼梯上的步频、步幅、步速特性,建立了乘客个体的步频、步幅、步速基本特性间的关系模型,以及乘客密度对乘客的步频、步幅、步速的影响关系模型;在分析不同属性的乘客对楼梯与自动扶梯的选择特性基础上,采用逐步回归分析方法,提取影响乘客选择的关键因素,建立了乘客对楼梯与自动扶梯的选择模型;根据观测的无中央分隔措施条件下通道内双向乘客的走行特性,提出了反映乘客微观交通特性的元胞更新规则,建立了基于蚁群算法的元胞自动机模型,对通道内双向乘客进行了仿真研究。
     在乘客宏观交通特性研究方面,在大量观测的乘客流量、速度、密度数据的基础上,分析了城市轨道交通乘客流在通道内及楼梯上的流量、速度、密度交通特性,建立了水平通道内及楼梯上的乘客流量、速度、密度关系模型;根据乘客在站台上的宏观分布数据,分析了乘客在站台上的分布规律,探究影响乘客选择站台候乘位置的主要因素,建立了乘客候乘位置选择模型;根据乘客的上下车时间特性数据,分析了乘客的上下车特性,建立了基于乘客流分层特性的乘客上下车时间模型;根据出站乘客在楼梯出口处拥挤产生及演化规律,研究了乘客在出站楼梯处的排队机理;基于表明偏好的调查理念,采用乘客感知的问卷调查方法,对城市轨道交通站台的服务水平进行调查,根据调查结果,给出不同等级服务水平的隶属度曲线,用线性插值的方法计算得到城市轨道交通站台服务水平的划分标准。
Field data of passenger traffic characteristics about exit, entrance, transfer, waiting and boarding facilities etc. have been collected as the basic step in urban rail transit. From the point of individual and group passenger, the micro-characteristics and macro-characteristics were analyzed respectively. Then the models reflecting the corresponding characteristics were established.
     In the aspect of analyzing passengers' micro-characteristics, firstly, passengers' step frequencies, step size, walking speed characteristics were explored in urban rail transit stairs and passages with different slopes and the relationship models between them were established by taking advantage of data mining method. Following the models between pedestrian density and above basic characteristics was put forward, which can reflect the influence of pedestrian density on the basic characteristics of individuals. Secondly, after the characteristics analysis of choosing stairs or escalators by different passengers with different attributes, stepwise regression analysis method was employed in order to extract the key factors which can influence passengers' choices characteristic. Accordingly, the models of embodying passengers' stairs and escalators choice characteristics were set up. At last, in the light of field survey on walking characteristics of bi-direction passengers in urban rail transit passengers without central partition, the update rules of the cellular, which can reflect the passengers' micro-characteristics, were put forward and the cellular automata (CA) model based on the ant colony algorithms was established as well, and then the simulation of bi-direction passengers in urban rail transit passages were carried out.
     In the aspect of analyzing passengers' macro-characteristics, based on numerous field data of passenger flow, speed and density surveyed in urban rail transit passages and stairs, the traffic characteristics of passenger flows in the passages and on the stairs were analyzed and the relationship models between them were also established. In addition, according to field data of the passengers' distribution on the urban rail transit platforms, the distribution rules of passengers on urban rail transit platforms were analyzed and the critical factors influencing the distribution of passengers along the platform were selected too, and then the distribution model of waiting passengers on the platform was set up. Furthermore, in line with the field data of passengers boarding and alighting time, the passengers' characteristics of boarding and alighting were analyzed, and model reflecting the boarding and alighting characteristics was put forward, which based on the stratification phenomenon of the boarding and alighting passenger flow. After those studies, based on the field data of the outbound passengers surveyed at the exit of the platform, evolution process of crowding at the exits was analyzed, and the queuing mechanism of the outbound passengers at the exit stairs was studied. Finally, Level of Service (LOS) for the urban rail transit platform was investigated by using questionnaire survey method, which based on the concept of stating preferences and passengers' perceptions. Under the survey results, the membership curve of different LOS was drawn and the perceived LOS for the platform was classified into five levels based on the congestion level. Using linear interpolation, critical values of the pedestrian density corresponding to different LOS were calculated.
引文
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