Smart Parking: Enhancing Urban Mobility with Fog Computing and Machine Learning-Based Parking Occupancy Prediction  
[PERSON]  
1 Departamento de Ingenieria Electrica, Instituto de Ingenieria y Tecnologia, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez 32310, Mexico; [EMAIL_ADDRESS] (F.J.E. ); [EMAIL_ADDRESS] (G.B.) [PERSON]  
1 Departamento de Ingenieria Electrica, Instituto de Ingenieria y Tecnologia, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez 32310, Mexico; [EMAIL_ADDRESS] (F.J.E. ); [EMAIL_ADDRESS] (G.B.) [PERSON]  
1 Departamento de Ingenieria Industrial, FES Aragon, Universidad Nacional Autonoma de Mexico, Mexico 57171, Mexico  
[PERSON]  
allocating parking spaces is a critical concern for large- and medium-sized cities where high traffic and a growing demand for parking availability are necessary for daily life. Finding parking spaces is not only time-consuming but also stressful for drivers, especially in busy urban areas [5]. The issue of parking allocation has been a topic of active research, with several proposals emerging in various areas such as optimizing parking allocation, intelligent parking, and automatic detection of vacant spaces. However, one area of opportunity that has gained significant attention is the prediction of parking spaces, which is an essential aspect of an all-encompassing solution to the parking allocation problem. Accurately predicting the availability of parking spaces can significantly improve the efficiency of parking space utilization, reduce traffic congestion, and save time for drivers. This approach requires the use of advanced technologies such as machine learning and artificial intelligence to analyze real-time data on parking lot occupancy and traffic patterns. Several innovative solutions have been proposed to predict parking space availability, such as using sensors, cameras, and mobile applications. These technologies provide real-time information to drivers on parking availability and guide them to the nearest available parking spot. Moreover, the data gathered from these systems can be analyzed to generate insights into parking usage patterns, which can be used to optimize parking lot allocation and enhance parking space utilization. For instance, in ref. [6], the authors used a graph-convolutional neural network followed by a long short-term memory layer to extract features of traffic flow data and predict parking occupancy. Their work demonstrates better predictions for business areas than recreational locations. Another study [7] predicts the intent and trajectory of vehicles using a transformer model combined with convolutional neural networks trained with video data of persons driving in parking lots and traffic scenarios. In ref. [8] is proposed a deep learning architecture based on combining GRU with a graph convolutional network to extract both temporal and spatial correlation information. The GRU detects temporal information, while the graph convolutional network is incorporated into the GRU cell to extract spatial correlations. The research by ref. [3] involves a comparison of various prediction methods for parking spaces, including deep learning, standard machine learning, and classical methods. The methods they examined include long short-term memory from deep learning, seasonal autoregressive integrated moving average as a classical method, and the ensemble-based decision trees as a general method of deep learning. The study found that the ensemble method and the long short-term memory generally performed better in producing predictions with lower errors compared with the classic autoregressive-based method. The study conducted by ref. [9] compared the predictive performance of various models including linear regression, support vector machine, neural networks, and autoregressive integrated moving average. It was found that the support vector machine model offered the most stable and accurate predictions among the models reviewed.