| 高速铁路动卧旅客对价格敏感度的识别与研究 |
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| 引用本文： | 王煜,徐彦,方伟,王亮.高速铁路动卧旅客对价格敏感度的识别与研究[J].铁路计算机应用,2019,28(3):12-15. |
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| 作者姓名： | 王煜 徐彦 方伟 王亮 |
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| 作者单位： | 1. 中国铁道科学研究院集团有限公司 电子计算技术研究所, 北京 10081; |
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| 基金项目： | 中国铁路总公司科技研究开发计划课题（2016X005-B） |
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| 摘 要： | 计算、评分铁路旅客群体对价格的敏感度,并在此基础上实现旅客群体分类,对于新线开通票价制定、既有线票价浮动测算、丰富常旅客营销手段、预售期票价折扣实施等方面工作具有重要参考意义。选取多次乘坐高速铁路(简称:高铁)动卧的旅客为样本对象,根据其在票价动态调整开始实施后出行行为的变化,运用K-means聚类算法和BP神经网络,将每位高铁动卧旅客对价格的敏感度进行识别和评价,并最终将所有旅客群体分成3类。结果表明,该方法能够较好地评价旅客价格敏感度,准确识别出价格敏感度较高的旅客群体,为铁路旅客运输利用价格手段实现"削峰填谷"、减少客流波动、提高经营效益的市场化目标提供助力。
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| 关 键 词： | 铁路旅客运输 K-MEANS聚类 BP神经网络 价格敏感度 高铁动卧旅客 |
| 收稿时间： | 2018-06-26 |
Identification and research on price sensitivity of high-speed railway EMU sleeper passengers |
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| WANG Yu,XU Yan,FANG Wei,WANG Liang.Identification and research on price sensitivity of high-speed railway EMU sleeper passengers[J].Railway Computer Application,2019,28(3):12-15. |
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| Authors: | WANG Yu XU Yan FANG Wei WANG Liang |
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| Affiliation: | 1. Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China;2. China Railway, Beijing 100844, China |
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| Abstract: | Calculating and grading the sensitivity of railway passenger groups to price, and implementing the classification of passenger groups on this basis have important reference significance for the establishment of new line ticket prices, the floating calculation of existing line ticket prices, the enrichment of frequent passenger marketing means, the implementation of advance ticket discounts and so on. Taking passengers who had repeatedly taken highspeed railway EMU sleeper as the sample, based on the change of travel behavior after dynamic adjustment of ticket prices, this paper used K-means clustering and BP neural network to identify and evaluate the price sensitivity of each recumbent passenger, and eventually divided the recumbent passenger group into three categories. The results show that this method can better evaluate the passenger price sensitivity, accurately identify the passenger groups with higher price sensitivity, and provide help for railway passenger transportation to achieve the goal of "cutting peak and filling valley" by price means, reduce passenger flow fluctuation and improve the market-oriented operation efficiency. |
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| Keywords: | railway passenger transportation K-means clustering BP neural network price sensitivity high-speed railway EMU sleeper passengers |
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