Locating available on-street parking in busy urban areas is often frustrating and time-consuming, particularly during peak hours. Inefficient parking searches contribute to traffic congestion, increased fuel consumption, and higher carbon emissions, making real-time parking availability information essential for smart urban mobility. This paper presents FedPark, a privacy-preserving crowdsensing system that leverages drivers’ smartphone sensors to monitor parking space occupancy. FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. Each local model learns from a user’s driving patterns without sharing personal data, ensuring enhanced privacy protection. Instead of transmitting raw data, only model updates are shared, reducing communication overhead and security risks. FedPark is evaluated under different urban conditions such as different numbers of drivers and destinations. The evaluation demonstrates that FedPark achieves a high level of accuracy at 98.31%, recall of 98.31%, precision of 98.34%, and an F1-score of 98.31% comparable to centralized machine learning models, including XGBoost, Random Forest, CatBoost, CNN, and ensemble classifiers while preserving driver privacy.

Studies have shown that in today’s urban areas, drivers lose a significant amount of time searching for available on-street parking spaces. Cruising drivers cause numerous problems, such as wasting gasoline and emitting gasses that lead to air pollution. To solve this issue, the parking industry and academia have made great efforts to lessen cruising drivers’ problems by providing on-street parking management solutions that can help enhance the efficient use of limited free on-street parking spaces. However, these solutions have two main limitations, scalability and privacy. This paper proposes a systematic literature review that examines the on-street parking management solutions that are currently in use, with a particular focus on their scalability and privacy limitations. According to the findings, there is a growing interest in on-street parking management solutions; however, the scalability of the systems used is a significant challenge since the servers that collect and manage parking availability have to perform intensive computation and communication with the drivers. Additionally, privacy concerns are a major issue, as the solutions often collect and store personal information such as drivers’ locations. The review concludes with recommendations for future research and development of these solutions to address both limitations and promote their widespread adoption.