Discovering Travelers’ Purchasing Behavior from Public Transport Data

In recent years, the demand for collective mobility services is characterized by a significant growth. The long-distance coach market has undergone an important change in Europe since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), aimed at discovering the factors that influence travelers in booking and purchasing a bus ticket. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between travel features and the purchase of a ticket. Such rules are then used to train a machine learning model for predicting whether a user will buy or not a ticket. The results obtained by this study reveal that factors such as occupancy rate, fare of a ticket, and number of days passed from booking to departure, have significant influence on traveler’s buying decisions. The methodology reaches an accuracy of 93% in forecasting the purchase of a ticket, showing the effectiveness of the proposed approach and the reliability of results.