MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction

• Published in: IEEE transactions on intelligent transportation systems 2022-07, Vol.23 (7), p.7244-7257
• Main Authors: Zhao, Dong, Ju, Chen, Zhu, Guanzhou, Ning, Jing, Luo, Dan, Zhang, Desheng, Ma, Huadong
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Availability; Data models; Feature extraction; graph convolutional neural network; long short-term memory network; Meters; Monitoring; Parking; Parking availability prediction; Predictive models; Real time; Real-time systems; Resource utilization; Sensors; spatial-temporal data; Traffic; Urban areas
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3067675

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.