交通运输系统工程与信息 ›› 2016, Vol. 16 ›› Issue (5): 33-38.

• 综合交通运输体系论坛 • 上一篇    下一篇

基于BP 神经网络的铁路客运服务质量评价研究

杨国元,史天运*,张秋亮   

  1. 中国铁道科学研究院电子计算技术研究所,北京100081
  • 收稿日期:2016-03-28 修回日期:2016-07-04 出版日期:2016-10-25 发布日期:2016-10-25
  • 作者简介:杨国元(1982-),男,甘肃武威人,助理研究员,博士.
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China(61374059);中国铁道科学研究院基金项目/Fund Project of China Academy of Railway Sciences (J2015DZ015).

Railway Passenger Service Quality Evaluation Based on BP Neural Network

YANG Guo-yuan, SHI Tian-yun, ZHANG Qiu-liang   

  1. Institute of Computing Technologies, China Academy of Railway Sciences, Beijing 100081, China
  • Received:2016-03-28 Revised:2016-07-04 Online:2016-10-25 Published:2016-10-25

摘要:

为了客观、准确地评价铁路客运服务质量,从旅客感知角度出发,构建了评价 指标体系,建立了BP神经网络求解模型,提出了基于BP算法的铁路客运服务质量评价 算法.通过对网络的训练,使得网络输出达到了期望的精度,满足了客运服务质量评价的 要求.最后通过对铁路旅客的调查问卷实例,从均方误差、统计识别率方面进行了分析、仿 真,对客运服务质量进行综合评价,并与模糊综合评价法进行了对比.实验结果表明,本文 构建的评价指标体系科学、合理,提出的评价算法收敛速度快、误差小,评价结果能够有 效地反映客运服务质量水平,为提高和改进铁路客运服务质量提供参考依据.

关键词: 铁路运输, BP神经网络, 客运服务质量, 评价体系, 评价指标

Abstract:

In order to objectively and accurately evaluate the railway passenger service quality, an evaluation system is built from the perspective of passenger perception. Railway passenger service quality evaluated algorithm and BP evaluated network model of solving are built based on BP algorithm. Through the network training, the network output reached a desired accuracy and met the requirements of passenger service quality evaluation. Finally, through the railway passenger questionnaire examples, it is analyzed and simulated from the aspects of the mean square error, statistical the recognition rate. Passenger service quality is comprehensive evaluated and compared with fuzzy comprehensive evaluation method. Experimental results show that the evaluation index system built is scientific and reasonable .The convergence is faster and the error is smaller of evaluation algorithm proposed. It can effectively reflect the quality of passenger service and provide reference for advancing and improving passenger service quality

Key words: railway transportation, BP neural network, passenger service quality, evaluation system, evaluation index

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