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Analyzing high speed rail passengers' train choices based on new online booking data in China

机译:根据中国新的在线预订数据分析高铁乘客的火车选择

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摘要

This study explores two nonparametric machine learning methods, namely support vector regression (SVR) and artificial neural networks (ANN), for understanding and predicting high-speed rail (HSR) travelers’ choices of ticket purchase timings, train types, and travel classes, using ticket sales data. In the train choice literature, discrete choice analysis is the predominant approach and many variants of logit models have been developed. Alternatively, emerging travel choice studies adopt non-utility-based methods, especially nonparametric machine learning methods including SVR and ANN, because (1) those methods do not rely on assumptions on the relations between choices and explanatory variables or any prior knowledge of the underlying relations; (2) they have superb capabilities of iteratively identifying patterns and extracting rules from data. This paper thus contributes to the HSR train choice literature by applying and comparing SVR and ANN with a real-world case study of the Shanghai-Beijing HSR market in China. A new normalized metric capturing both the load factor and the booking lead time is proposed as the target variable and several train service attributes, such as day of week, departure time, travel time, fare, are identified as input variables. Computational results demonstrate that both SVR and ANN can predict the train choice behavior with high accuracy, outperforming the linear regression approach. Potential applications of this study, such as rail pricing reform, have also been identified.
机译:这项研究探索了两种非参数机器学习方法,即支持向量回归(SVR)和人工神经网络(ANN),用于了解和预测高铁(HSR)旅客的购票时间,火车类型和旅行等级的选择,使用门票销售数据。在火车选择文献中,离散选择分析是主要方法,并且已经开发了logit模型的许多变体。或者,新兴的旅行选择研究采用基于非效用的方法,尤其是非参数机器学习方法,包括SVR和ANN,因为(1)这些方法不依赖于选择与解释变量之间关系的假设或对基础知识的任何先验知识关系(2)它们具有迭代识别模式和从数据中提取规则的出色能力。因此,本文通过应用SVR和ANN并将其与中国上海-北京高铁市场的实际案例进行比较,从而为高铁列车选择文献做出了贡献。提出了一种既包含负载因子又包含预订提前期的归一化度量,将其作为目标变量,并将几个火车服务属性(例如,星期几,出发时间,旅行时间,票价)确定为输入变量。计算结果表明,SVR和ANN都可以高精度预测列车选择行为,优于线性回归方法。还确定了这项研究的潜在应用,例如铁路价格改革。

著录项

  • 来源
    《Transportation research》 |2018年第12期|96-113|共18页
  • 作者单位

    China Railway Shanghai Group;

    Department of Industrial and Manufacturing Engineering, FAMU-FSU College of Engineering, Florida State University;

    Department of Civil and Environmental Engineering, University of Maryland, College Park;

    College of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Tongji University;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    High-speed rail; Train choice; Revenue management; Online booking data;

    机译:高铁;火车选择;收入管理;在线预订数据;

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