Analysis of High-Speed Railway Passenger's Travel Choice Behavior Based on Deep Learning Model

Abstract

The analysis of railway passengers' travel choice behavior plays an import role in railway passenger product design and transport organization. Many researchers focued on it and lots of models and methods had been proposed. With the rapid development of information technology in recent years, deep learning models are more and more widely used in many research fields, and also applied in this research. Based on the OD train ticket data of Beijing-Shanghai high-speed railway line, it studied travel choice behavior of passengers in Beijing-Shanghai high-speed railway line during the weekday, and then took attributes of ticket data (arrive-departure station, arrival-departure time, etc.) as the mapping features to study passengers' choice of different trains, The result shows that the method given by the research has quite high fitting accuracy.