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To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. The model was validated using historical parking transaction volume data from all on-street parking lots in Nanshan District, Shenzhen, from September 2018 to March 2019, and its superiority was verified through comparative experiments with benchmark models. The results show that the MGCN\u2013Transformer model has a MAE, RMSE, and R2 error index of 0.26, 0.42, and 95.93%, respectively, in the multi-step prediction task of parking demand, demonstrating its superior predictive accuracy compared to other benchmark models.<\/jats:p>","DOI":"10.3390\/systems12110487","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T08:06:32Z","timestamp":1731571592000},"page":"487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer"],"prefix":"10.3390","volume":"12","author":[{"given":"Yixiong","family":"Zhou","sequence":"first","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-4955","authenticated-orcid":false,"given":"Xiaofei","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0858-1482","authenticated-orcid":false,"given":"Xingchen","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-3712","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"ref_1","unstructured":"Traffic Management Bureau (2024, January 02). 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Part B Methodol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"58594","DOI":"10.1109\/ACCESS.2020.2976433","article-title":"Short-term parking demand prediction method based on variable prediction interval","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","first-page":"533","article-title":"Evaluation of Parking Space Occupancy Prediction Methods","volume":"45","author":"Tang","year":"2017","journal-title":"Tongji Univ. J. (Nat. Sci. Ed.)"},{"key":"ref_7","unstructured":"Zheng, Y., Rajasegarar, S., and Leckie, C. (2015, January 7\u20139). Parking availability prediction for sensor-enabled car parks in smart cities. 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