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Bus Passenger Load Prediction: Challenges from an Industrial Experience

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Web and Wireless Geographical Information Systems (W2GIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13238))

Abstract

In times of ongoing pandemic outbreak, public transportation systems organisation and operation have been significantly affected. Among others, the necessity to implement in-vehicle social distancing has fostered new requirements, such as the possibility to know in advance how many people will likely be on a public bus at a given stop. This is very relevant for both potential passengers waiting at a stop, and for decision makers of a transit company, willing to adapt the operational planning. Within the domain of data-driven Intelligent Transportation Systems (ITS), some research activities are being conducted towards Bus Passenger Load (BPL) predictions, with contrasting results. In this paper we report on an academic/industrial experience we conducted to predict BPL in a major Italian city, using real-world data. In particular, we describe the difficulties and challenges we had to face in the data processing and mining steps, due to multiple data sources, with noisy data. As a consequence, in this paper we highlight to the ITS community the need of more advanced techniques and approaches suitable to support the instantiation of a data analytic pipeline for BPL prediction.

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Author information

Authors and Affiliations

  1. University of Naples Federico II, DIETI, via Claudio 21, 80125, Naples, Italy

    Flora Amato, Sergio Di Martino, Nicola Mazzocca & Franca Rocco di Torrepadula

  2. Hitachi Rail, via Argine 425, 80147, Naples, Italy

    Davide Nardone & Paolo Sannino

Authors
  1. Flora Amato
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  2. Sergio Di Martino
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  3. Nicola Mazzocca
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  4. Davide Nardone
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  5. Franca Rocco di Torrepadula
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  6. Paolo Sannino
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Corresponding author

Correspondence to Franca Rocco di Torrepadula .

Editor information

Editors and Affiliations

  1. Institute of Science and Technology Austria, Klosterneuburg, Austria

    Farid Karimipour

  2. Universität Konstanz, Konstanz, Baden-Württemberg, Germany

    Sabine Storandt

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Amato, F., Di Martino, S., Mazzocca, N., Nardone, D., di Torrepadula, F.R., Sannino, P. (2022). Bus Passenger Load Prediction: Challenges from an Industrial Experience. In: Karimipour, F., Storandt, S. (eds) Web and Wireless Geographical Information Systems. W2GIS 2022. Lecture Notes in Computer Science, vol 13238. Springer, Cham. https://doi.org/10.1007/978-3-031-06245-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-06245-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06244-5

  • Online ISBN: 978-3-031-06245-2

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