| Title | Parking Prediction in Smart Cities: A Survey |
|---|---|
| ID_Doc | 41672 |
| Authors | Xiao, X; Peng, ZY; Lin, YQ; Jin, ZL; Shao, W; Chen, R; Cheng, N; Mao, GQ |
| Title | Parking Prediction in Smart Cities: A Survey |
| Year | 2023 |
| Published | Ieee Transactions On Intelligent Transportation Systems, 24, 10 |
| Abstract | With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities. |
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