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Explaining Taxi Demand Prediction Models Based on Feature Importance

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Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1947))

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Abstract

The prediction of city-wide taxi demand is used to proactively relocate idle taxis. Often neural network-based models are applied to tackle this problem, which is difficult due to its multivariate input and output space. As these models are composed of multiple layers, their predictions become opaque. This opaqueness makes debugging, optimising, and using the models difficult. To address this, we propose the usage of eXplainable AI (XAI) – feature importance methods.

In this paper, we build and train four city-wide taxi demand prediction models of commonly used neural network types on the New York City Yellow Taxi Trip data set. To explain their predictions, we select three existing XAI techniques – reduced Layerwise Relevance Propagation, Local Interpretable Model-agnostic Explanation, and Shapely Additive Explanations – and enable their usage on the specified problem. In addition, we propose a suite of five quantitative evaluation metrics suitable for explaining models that tackle regression problems with multivariate input and output space. Lastly, we compare the selected XAI techniques through the proposed evaluation metrics along four real-world scenarios.

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Author information

Authors and Affiliations

  1. Institute for Informatics, Clausthal University of Technology, Julius-Albert-Straße 4, 38578, Clausthal-Zellerfeld, Germany

    Eric Loff, Sören Schleibaum & Jörg P. Müller

  2. Institute for Mathematics, Clausthal University of Technology, Erzstraße 1, 38678, Clausthal-Zellerfeld, Germany

    Benjamin Säfken

Authors
  1. Eric Loff
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  2. Sören Schleibaum
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  3. Jörg P. Müller
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  4. Benjamin Säfken
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Corresponding author

Correspondence to Eric Loff .

Editor information

Editors and Affiliations

  1. Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk

  2. Warsaw University of Technology, Warsaw, Poland

    Przemysław Biecek

  3. Warsaw University, Warsaw, Poland

    Neo Christopher Chung

  4. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  5. AGH University of Science and Technology, Kraków, Poland

    Paweł Skruch

  6. AGH University of Science and Technology, Kraków, Poland

    Joanna Jaworek-Korjakowska

  7. University of Huddersfield, Huddersfield, UK

    Simon Parkinson

  8. University of Huddersfield, Huddersfield, UK

    Alexandros Nikitas

  9. Universität Osnabrück, Osnabrück, Germany

    Martin Atzmüller

  10. University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr

  11. University of Bamberg, Bamberg, Germany

    Ute Schmid

  12. Jagiellonian University, Kraków, Poland

    Szymon Bobek

  13. Jožef Stefan Institute, Ljubljana, Slovenia

    Nada Lavrac

  14. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Marieke Peeters

  15. Rotterdam University of Applied Sciences, Rotterdam, The Netherlands

    Roland van Dierendonck

  16. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Saskia Robben

  17. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  18. Istanbul Technical University, Istanbul, Türkiye

    Gülgün Kayakutlu

  19. Wroclaw University of Economics and Business, Wrocław, Poland

    Mieczyslaw Lech Owoc

  20. University of Galway, Galway, Ireland

    Karl Mason

  21. University of Galway, Galway, Ireland

    Abdul Wahid

  22. University of Calabria, Rende, Italy

    Pierangela Bruno

  23. University of Calabria, Rende, Italy

    Francesco Calimeri

  24. Marche Polytechnic University, Ancona, Italy

    Francesco Cauteruccio

  25. University of Calabria, Rende, Italy

    Giorgio Terracina

  26. University of Bamberg, Bamberg, Germany

    Diedrich Wolter

  27. Coburg University of Applied Sciences, Coburg, Germany

    Jochen L. Leidner

  28. FAU Erlangen-Nürnberg, Erlangen, Germany

    Michael Kohlhase

  29. University of Leeds, Leeds, UK

    Vania Dimitrova

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Loff, E., Schleibaum, S., Müller, J.P., Säfken, B. (2024). Explaining Taxi Demand Prediction Models Based on Feature Importance. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1947. Springer, Cham. https://doi.org/10.1007/978-3-031-50396-2_15

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  • DOI: https://doi.org/10.1007/978-3-031-50396-2_15

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