Issue |
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
|
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Article Number | 00065 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/e3sconf/202346900065 | |
Published online | 20 December 2023 |
Deep learning for parking spaces prediction in the context of smart and sustainable cities: A systematic literature review
LabSiV Laboratory, Ibn Zohr University, Agadir, 80080, Morocco.
* Corresponding author: abdoulnasser.hamidou@edu.uiz.ac.ma
The search for solutions to mitigate traffic congestion is a major challenge for densely populated cities. Studies have shown that more than 40% of traffic jams are caused by prolonged searching for parking spaces in crowded cities. Therefore, predicting the availability of parking spaces in advance is a crucial step in helping drivers quickly find free spaces and thus reduce traffic jams and their negative impacts on the environment, economy, and public health. Various approaches have been proposed to solve traffic congestion related problems. Deep learning, a technique in machine learning, has seen increasing use and has shown much effectiveness compared to other machine learning techniques for predicting parking space availability. In this study, we analyzed the use of deep learning techniques through a systematic literature review. The review process included formulating the research question, establishing search strategies, as well as data extraction and analysis. As a result, we identified four major families of deep learning techniques commonly used for predicting parking space availability. Additionally, we observed that recurrent neural networks and convolutional neural networks are the most widely used techniques.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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