文章摘要
刘 悦,冯旭杰,贾文峥,李松峰,沙 茜.基于乘客群体特征的城市轨道交通满意度研究[J].交通运输研究,2020,6(4):95-101.
基于乘客群体特征的城市轨道交通满意度研究
Satisfaction in Urban Rail Transit Based on Characteristics of Passenger Groups
  
DOI:
中文关键词: 城市轨道交通  满意度  乘客群体特征  主成分分析  差异性分析
英文关键词: urban rail transit  satisfaction  characteristic of passenger group  principal component analysis  difference analysis
基金项目:中央级公益性科研院所基本科研业务费项目(20184805)
作者单位
刘 悦 交通运输部科学研究院城市轨道交通运营安全管理技术及装备交通运输行业研发中心 
冯旭杰 交通运输部科学研究院城市轨道交通运营安全管理技术及装备交通运输行业研发中心 
贾文峥 交通运输部科学研究院城市轨道交通运营安全管理技术及装备交通运输行业研发中心 
李松峰 交通运输部科学研究院城市轨道交通运营安全管理技术及装备交通运输行业研发中心 
沙 茜 交通运输部科学研究院城市轨道交通运营安全管理技术及装备交通运输行业研发中心 
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中文摘要:
      为提高城市轨道交通服务质量、优化乘客乘车体验、提升城市轨道交通在公共交通中的竞争力,对城市轨道交通乘客满意度开展研究。首先,选取乘客特征因素,引入皮尔逊积矩相关系数来分析乘客满意度与乘客特征因素的相关关系,提取对满意度存在显著影响的乘客特征因素。然后,运用主成分分析法刻画乘客群体特征,据此对乘客进行分类,并采用克鲁斯卡尔-沃利斯检验法来分析其满意度的差异性。基于此,对某城市地铁A号线2019年乘客满意度调查数据进行了分析,结果显示:地铁A号线乘客的出行频率、出行目的和收入水平与乘客满意度显著相关。通过主成分分析将乘客划分为常乘客与非常乘客,发现两者对城市轨道交通服务的关注点不同,常乘客更关注乘车效率,非常乘客更关注信息指引准确性,运营单位应针对不同乘客群体展开差异性服务。
英文摘要:
      In order to improve the service quality of urban rail transit, optimize passenger experience and improve the competitiveness of urban rail transit in public transportation, the study on passenger satisfaction was put forward. First, the passenger characteristic factors were chosen, and the Pearson Product-Moment Correlation Coefficient was applied to analyze the correlation between passenger satisfaction and passenger characteristic factors and extract the passenger characteristic factors which have significant impact on satisfaction. Then according to the Principal Component Analysis method, the main characteristic factors of passengers were described, and the passengers were classified. The difference in satisfaction of different passenger groups was analyzed by Kruskal-Wallis(K-W) Test. On this basis, the passenger satisfaction survey data of Metro Line A of one city in 2019 was analyzed. The results showed that for passengers of Metro Line A, passenger travel frequency, travel purpose and income level were significantly related to passenger satisfaction. Passengers were divided into frequent passenger groups and non-frequent passenger groups through Principal Component Analysis. The two groups had different concerns about urban rail transit services. Frequent passengers paid more attention to riding efficiency, while non-frequent passengers paid more attention to accuracy of information guidance. Operators should provide different services for different passenger groups.
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