Analyzing high speed rail passengers' train choices based on new online booking data in China. (December 2018) - Sun, Yanshuo; Jiang, Zhibin; Gu, Jinjing; Zhou, Min; Li, Yeming; Zhang, Lei
Highlights: We study high-speed rail travelers' train choices for revenue management purposes using online booking data. A new metric for quantifying a transport product's attractiveness is proposed incorporating booking lead time. We apply two nonparametric machine learning methods requiring no utility maximization assumption. Performances of SVR and ANN are comparable, and both methods clearly outperform the benchmark method. Choice preferences of travelers in the Shanghai-Beijing rail market in China are revealed for the first time. Abstract: 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.