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Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain

Author

Listed:
  • Leticia Monje

    (Faculty of Statistics, Complutense University Puerta de Hierro, 28040 Madrid, Spain)

  • Ramón A. Carrasco

    (Department of Marketing, Faculty of Commerce and Tourism Complutense, University of Madrid, 28003 Madrid, Spain)

  • Carlos Rosado

    (Computer Science Department, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

  • Manuel Sánchez-Montañés

    (Computer Science Department, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

Abstract

Time series forecasting of passenger demand is crucial for optimal planning of limited resources. For smart cities, passenger transport in urban areas is an increasingly important problem, because the construction of infrastructure is not the solution and the use of public transport should be encouraged. One of the most sophisticated techniques for time series forecasting is Long Short Term Memory (LSTM) neural networks. These deep learning models are very powerful for time series forecasting but are not interpretable by humans (black-box models). Our goal was to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. Our case study was one of the most demanded bus lines of Madrid. We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable Artificial Intelligent (XAI) model without losing precision.

Suggested Citation

  • Leticia Monje & Ramón A. Carrasco & Carlos Rosado & Manuel Sánchez-Montañés, 2022. "Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1428-:d:800468
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    References listed on IDEAS

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    2. Wusheng Liu & Qian Tan & Wei Wu, 2020. "Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, December.
    3. Gabriel Marín Díaz & Ramón Alberto Carrasco & Daniel Gómez, 2021. "RFID: A Fuzzy Linguistic Model to Manage Customers from the Perspective of Their Interactions with the Contact Center," Mathematics, MDPI, vol. 9(19), pages 1-27, September.
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    Cited by:

    1. Violeta Lukic Vujadinovic & Aleksandar Damnjanovic & Aleksandar Cakic & Dragan R. Petkovic & Marijana Prelevic & Vladan Pantovic & Mirjana Stojanovic & Dejan Vidojevic & Djordje Vranjes & Istvan Bodol, 2024. "AI-Driven Approach for Enhancing Sustainability in Urban Public Transportation," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    2. Gabriel Marín Díaz & Raquel Gómez Medina & José Alberto Aijón Jiménez, 2024. "Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification," Mathematics, MDPI, vol. 12(18), pages 1-27, September.
    3. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    4. Li, Xin & Xu, Yechi & Law, Rob & Wang, Shouyang, 2024. "Enhancing tourism demand forecasting with a transformer-based framework," Annals of Tourism Research, Elsevier, vol. 107(C).

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