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Congestion Degree Classification for Urban Rail Transit System Based on Hierarchical Cluster Analysis

机译：基于层次聚类分析的城市轨道交通系统拥堵度分类

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Considering the congestion in the metro, the operator usually uses congestion degree information to guide passenger flow which can alleviate the operation pressure. However, the current congestion degree classifications are mostly based on subjective judgment, without fully considering passengers' psychological perceptions under different load factors. This paper aims to set up a reasonable classification of congestion degree for urban rail transit system. First, passengers' perception differences of congestion degree are analyzed from the views of different properties (i.e., age, gender, travel purpose, and travel time), based on the questionnaire data collected in Beijing Metro. Then, the distributions of passengers' congestion degree perception under different load factors are calculated, and the load factors are clustered based on hierarchical cluster analysis. Finally, the congestion degree for urban rail transit is classified into four levels (i.e., comfortable, slight crowded, crowded, and heavy crowded), corresponding to the load factor interval (0, 40%), (40%, 75%), (75%, 100%), and (100%, +∞).
机译：考虑到地铁中的拥堵，运营商通常使用拥堵度信息来引导客流，从而减轻运营压力。然而，目前的拥挤程度分类主要基于主观判断，没有充分考虑不同负荷因素下乘客的心理认知。本文旨在为城市轨道交通系统建立合理的拥堵度分类。首先，根据北京地铁收集的问卷数据，从不同属性（即年龄，性别，出行目的和出行时间）的角度分析乘客的拥挤程度感知差异。然后，计算了不同负荷因子下乘客的拥挤度感知分布，并基于层次聚类分析对负荷因子进行聚类。最后，城市轨道交通的拥挤度分为四个级别（即舒适，轻度拥挤，拥挤和重度拥挤），分别对应于负荷系数区间（0、40％），（40％，75％）， （75％，100％）和（100％，+∞）。

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