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Real-Time Parking Availability Classification on a Large-Area Scale

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Innovations in Smart Cities Applications Volume 7 (SCA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 906))

Abstract

In urban areas around the world, drivers face the daily challenge of finding a parking space. Unfortunately, these sought-after spaces, located close to their destination, are often either impossible to find, or excessively expensive, resulting in longer search times and increased congestion in city centers. The answer to this persistent problem is an intelligent parking solution. They provide drivers with real-time access to information on parking space availability, gathered through various sensing techniques such as crowdsourcing, parking meters and sensors. Some of these systems also offer opportunistic services, such as forecasts, to adapt to unforeseen dynamic situations. Drivers’ biggest concern is find ing a spot, not knowing the exact number of available spaces or availability rate. Typically, these two parameters are estimated using regression or image processing techniques. While such solutions guarantee high predictive accuracy, their large-scale deployment is hampered by computational and data collection costs. This paper therefore proposes a new approach combining clustering and classification models to predict parking availability. Our aim is to test new methods that are relatively simple and less expensive in terms of both processing costs and amount of training data. Experimental results have proved promising, with accuracy predictions exceeding 0.84.

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Author information

Authors and Affiliations

  1. EIGSI, La Rochelle-Casablanca, 17041-20410, Casablanca, Morocco

    Youssef Filali, Hanae Errousso & Nihad Aghbalou

  2. National and High School of Electricity and Mechanic, HASSAN II University, 8118, Casablanca, Morocco

    Hanae Errousso

  3. IEVIA Team, IMAGE Laboratory, Department of Sciences, ENS, Moulay Ismail University of Meknes, Meknes, Morocco

    El Arbi Abdellaoui Alaoui

  4. Faculty of Sciences Dhar-Mahraz, 30000, Fez, Morocco

    Youssef Filali & My Abdelouahed Sabri

Authors
  1. Youssef Filali
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  2. Hanae Errousso
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  3. Nihad Aghbalou
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  4. El Arbi Abdellaoui Alaoui
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  5. My Abdelouahed Sabri
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Corresponding author

Correspondence to El Arbi Abdellaoui Alaoui .

Editor information

Editors and Affiliations

  1. Computer Science and Smart systems Laboratory, Faculty of Science and Techniques of Tangier, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Computer Science and Smart systems Laboratory, Faculty of Science and Techniques of Tangier, Abdelmalek Essaadi University, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. École Spéciale des Travaux Publics, Paris, France

    Rani El Meouche

  4. Computer Engineering Department, Karabük University, Karabük, Türkiye

    İsmail Rakıp Karaș

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Filali, Y., Errousso, H., Aghbalou, N., Alaoui, E.A.A., Sabri, M.A. (2024). Real-Time Parking Availability Classification on a Large-Area Scale. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 906. Springer, Cham. https://doi.org/10.1007/978-3-031-53824-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-53824-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53823-0

  • Online ISBN: 978-3-031-53824-7

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