[1]吴静娴,杨敏.基于贝叶斯网络的城市常规公交服务满意度分析模型[J].东南大学学报(自然科学版),2017,47(5):1042-1047.[doi:10.3969/j.issn.1001-0505.2017.05.032]
 Wu Jingxian,Yang Min.Assessment of passenger satisfaction with urban bus service quality using Bayesian networks[J].Journal of Southeast University (Natural Science Edition),2017,47(5):1042-1047.[doi:10.3969/j.issn.1001-0505.2017.05.032]
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基于贝叶斯网络的城市常规公交服务满意度分析模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第5期
页码:
1042-1047
栏目:
交通运输工程
出版日期:
2017-09-20

文章信息/Info

Title:
Assessment of passenger satisfaction with urban bus service quality using Bayesian networks
作者:
吴静娴杨敏
东南大学城市智能交通江苏省重点实验室, 南京 210096; 东南大学现代城市交通技术江苏高效协同创新中心, 南京 210096; 东南大学交通学院, 南京 210096
Author(s):
Wu Jingxian Yang Min
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
School of Transportation, Southeast University, Nanjing 210096, China
关键词:
贝叶斯网络 公交服务 乘客满意度 贝叶斯推理
Keywords:
Bayesian networks public transit service passenger satisfaction Bayesian inference
分类号:
U491
DOI:
10.3969/j.issn.1001-0505.2017.05.032
摘要:
为揭示城市公交服务指标之间的潜在相关性和分析各指标对常规公交整体满意度的影响度,以609份南京市常规公交服务调查问卷为数据基础,利用GTT算法和MLE算法,建立基于贝叶斯网络公交服务满意度分析模型,挖掘常规公交服务指标与乘客总体满意度之间的网络关系.模型检验结果显示,贝叶斯网络具有较高的适用性和有效性.由置信度传播算法获得的推理结果得到的7项主要影响因素中,候车时间的负效应-31.4%最为显著,车内卫生的正效应最为明显,高达33.7%,行车准点性和换乘便捷性各自具有较高的提升空间且正负影响域较大,分别为-26.6%~23.1%和-20.9%~27.3%,车内有无座椅、候车环境和空调的配备具有较高的绝对影响域,均高达55%.
Abstract:
To discover the underlying relationships among service aspects and assess their influences on passenger overall satisfaction with regular bus service, a Bayesian network(BN)was developed by the greedy thick thinning(GTT)and maximum likehood estimation(MLE)algorithms based on 609 questionnaires from 2013 regular bus service survey in Nanjing, China. The established model could elaborately capture the network relationships among service attributes and passenger overall satisfaction. The validation result proves that BN is more applicable and efficient. Among the 7 main contributors inferred by the belief propagation algorithm, the negative effect of waiting time is the most significant at -31.4% and the positive effect of onboard environment is the most significant at 33.7%. The punctuality and the convenience have larger influence ranges, from -26.6% to 23.1% and from -20.9% to 27.3%, respectively. The seat availability, environment at stations, and the provision of air-conditioning onboard are the most influential with the absolute ranges up to 55%.

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备注/Memo

备注/Memo:
收稿日期: 2016-12-12.
作者简介: 吴静娴(1987—),女,博士生;杨敏(联系人),男,博士,教授,博士生导师,yangmin@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51338003,51378120).
引用本文: 吴静娴,杨敏.基于贝叶斯网络的城市常规公交服务满意度分析模型[J].东南大学学报(自然科学版),2017,47(5):1042-1047. DOI:10.3969/j.issn.1001-0505.2017.05.032.
更新日期/Last Update: 2017-09-20