Abstract
In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent 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), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.
Highlights
The long-distance bus industry has traditionally been slow to evolve and is quite resistant to change
We compared the different pricing strategies, showing that a dynamic pricing strategy based on occupancy rate and number of days passed from booking to departure are capable of increasing the number of purchased tickets by 6% and the total revenue by 9%
This paper presented a methodology, called DA4PT, aimed at discovering the main factors influencing users in purchasing bus tickets both for training a machine learning model able to predict whether or not a user will buy a ticket, and for testing pricing strategies for maximizing the number of purchased tickets and the total revenue of a bus company
Summary
The long-distance bus industry has traditionally been slow to evolve and is quite resistant to change. FlixBus has immediately provided an advanced online ticket booking platform, which allows booking buses with great flexibility and convenience, and providing personalized offers for users by exploiting the large amount of data stored on its platform [3]. DA4PT allows analyzing large amounts of user-generated event logs from bus ticketing platforms for finding the correlation rules between booking factors and purchase of a ticket. Such rules are used: (i) for training machine learning models able to predict whether a user will buy or not a ticket, and (ii) for defining different dynamic pricing strategies with the purpose of increasing ticket sales on the platform and the total revenue
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