衡量学生对公交服务的满意程度:潜类分析法的应用

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引用次数: 0

摘要

以往的研究已经确定了普通公众对传统固定路线公交和共享乘车服务属性的满意度,但很少有研究将重点放在学生身上。本研究采用潜类聚类分析(LCCA),根据大学生对传统固定路线公交和共享乘车服务属性的满意程度,识别出大学生聚类,并使用德克萨斯州阿灵顿市大学生样本的潜类行为模型,探讨他们对共享乘车服务偏好的异质性。研究结果表明,年轻和低收入人群比年长和高收入人群更容易对按需共享乘车服务感到满意,女性比男性更容易对共享乘车服务感到满意,国内学生比留学生更容易对共享乘车服务感到满意。研究结果将为交通规划者提供新的见解,让他们了解社会人口因素对使用传统公交和按需共享乘车服务的人的满意度的重要影响,并帮助他们制定策略,使其服务对潜在乘客更具吸引力。
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Measuring students’ satisfaction levels for transit services: An application of latent class analysis
Past studies have identified the general public’s level of satisfaction with the service attributes of conventional fixed-route transit and ridesharing services, but few have limited their focus to students. This study employs latent class cluster analysis (LCCA) to identify clusters of university students, based on their satisfaction levels of the attributes of conventional fixed-route and ridesharing services, and uses a latent class behavioral model of a sample of university students in Arlington, Texas to explore the heterogeneity of their preferences toward ridesharing services. The results indicate that younger- and lower-income populations are more likely to be satisfied with on-demand ridesharing services than older- and higher-income populations, females are more likely to be satisfied with ridesharing services than males, and domestic students are more likely to be satisfied with ridesharing services than international students. The outcomes of the study will provide transportation planners with new insights about the significance of sociodemographic factors on the satisfaction level of those who use conventional transit and on-demand ridesharing services and will help them incorporate strategies that will make their services more attractive to their potential ridership.
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来源期刊
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
期刊最新文献
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