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首页> 外文期刊>Accident Analysis & Prevention >Metro passenger behaviors and their relations to metro incident involvement
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Metro passenger behaviors and their relations to metro incident involvement

机译:地铁乘客的行为及其与地铁事件参与的关系

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The frequent incidents caused by metro passengers in China suggest that it is necessary to explore the classification and effects of passenger behaviors and their relations to incident involvement. A metro passenger behavior questionnaire (MPBQ) and a metro station staff questionnaire (MSSQ), both comprising 32 behavior items, were developed and surveyed on a sample of metro passengers (N=579) and metro staff (N=99). Using the MPBQ the self-reported frequency of each aberrant behavior was measured and subjected to explanatory factor analysis, which revealed a three-factor solution on the 28 retained behavior items: transgressions, self-willed inattentions and abrupt violations. ANOVA was used to examine the effects of demographic and riding profile variables on different types of behaviors. The MSSQ was used to collect metro staff opinions on behavior frequency, severity and entities that might be affected, given that a specific behavior occurred. An importance hierarchy was established over the 32 identified behaviors to determine the most important riding behaviors. Finally, logistic regression showed that riding time, number of stops experienced by a passenger and, more importantly, transgressions and abrupt violations, were significant predictors of incident involvement. The possible explanations and implications of the findings might help in understanding passenger behaviors and targeting metro safety interventions in ways that promote safer operations. (C) 2015 Elsevier Ltd. All rights reserved.
机译:中国地铁乘客频繁发生的事件表明,有必要探讨乘客行为的分类和影响及其与事件参与的关系。制定了都包含32个行为项的地铁乘客行为调查表(MPBQ)和地铁站工作人员调查表(MSSQ),并对地铁乘客(N = 579)和地铁工作人员(N = 99)的样本进行了调查。使用MPBQ对每种异常行为的自我报告频率进行了测量,并进行了解释性因素分析,揭示了针对28个保留的行为项的三因素解决方案:过犯,自我遗忘和突然违规。方差分析用于检验人口统计学和骑行状况变量对不同类型行为的影响。考虑到发生了特定的行为,MSSQ用于收集地铁工作人员对行为频率,严重性和可能受到影响的实体的意见。在确定的32种行为上建立了重要性层次结构,以确定最重要的骑行行为。最后,逻辑回归分析表明,骑行时间,乘客经历的停留次数以及更重要的是,过犯和突如其来的违反行为,是事故发生的重要预测因子。研究结果的可能解释和含义可能有助于理解乘客的行为,并以促进更安全运营的方式针对地铁安全干预措施。 (C)2015 Elsevier Ltd.保留所有权利。

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