Open Access

ARTICLE

The Role of Deep Learning in Parking Space Identification and Prediction Systems

by Faizan Rasheed¹, Yasir Saleem², Kok-Lim Alvin Yau^3,*, Yung-Wey Chong^4,*, Sye Loong Keoh⁵

1 School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL109AB, UK
2 Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3FL, UK
3 Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, 43200, Selangor, Malaysia
4 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Penang, 11800, Malaysia
5 School of Computing Science, University of Glasgow Singapore, 737729, Singapore

* Corresponding Authors: Kok-Lim Alvin Yau. Email: ; Yung-Wey Chong. Email:

Computers, Materials & Continua 2023, 75(1), 761-784. https://doi.org/10.32604/cmc.2023.034988

Received 03 August 2022; Accepted 23 November 2022; Issue published 06 February 2023

Abstract

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

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Keywords

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