Deep learning for parking spaces prediction in the context of smart and sustainable cities: A systematic literature review

Abstract

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.