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Discovering Travelers’ Purchasing Behavior from Public Transport Data

  • Conference paper
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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

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Abstract

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.

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Authors and Affiliations

  1. DIMES, University of Calabria, Rende, Italy

    Francesco Branda, Fabrizio Marozzo & Domenico Talia

Authors
  1. Francesco Branda
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  2. Fabrizio Marozzo
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  3. Domenico Talia
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Corresponding author

Correspondence to Francesco Branda .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Branda, F., Marozzo, F., Talia, D. (2020). Discovering Travelers’ Purchasing Behavior from Public Transport Data. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_63

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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