{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:03:22Z","timestamp":1779311002196,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. The application of Graph Convolutional Networks (GCNs) in traffic forecasting has inspired the development of a spatial\u2013temporal model for grid-level prediction of the taxi demand\u2013supply imbalance. However, spatial\u2013temporal GCN prediction models conventionally capture only static inter-grid correlation features. This research aims to address the dynamic influences caused by taxi mobility and the variations of other transportation modes on the demand\u2013supply dynamics between grids. To achieve this, we employ taxi trajectory data and develop a model that incorporates dynamic GCN and Gated Recurrent Units (GRUs) to predict grid-level imbalances. This model captures the dynamic inter-grid influences between neighboring grids in the spatial dimension. It also identifies trends and periodic changes in the temporal dimension. The validation of this model, using taxi trajectory data from Shenzhen city, indicates superior performance compared to classical time-series models and spatial\u2013temporal GCN models. An ablation study is conducted to analyze the impact of various factors on the predictive accuracy. This study demonstrates the precision and applicability of the proposed model.<\/jats:p>","DOI":"10.3390\/ijgi13020034","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T04:35:40Z","timestamp":1706070940000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand\u2013Supply Imbalance Using GPS Trajectories"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2073-0433","authenticated-orcid":false,"given":"Haiqiang","family":"Yang","sequence":"first","affiliation":[{"name":"Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China"},{"name":"Shandong Key Laboratory of Industrial Control Technology, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihan","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Future, College of Physics, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/S0191-2615(01)00031-5","article-title":"Demand\u2013Supply Equilibrium of Taxi Services in a Network under Competition and Regulation","volume":"36","author":"Yang","year":"2002","journal-title":"Transp. 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