乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:24:40Z","timestamp":1732040680579},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871364"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["2019zzts881"],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51\u201346.15% and 2.45\u201349.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95\u201389.91% and 0.26\u201310.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.<\/jats:p>","DOI":"10.3390\/ijgi10070485","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T03:28:30Z","timestamp":1626406110000},"page":"485","source":"Crossref","is-referenced-by-count":173,"title":["A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting"],"prefix":"10.3390","volume":"10","author":[{"given":"Jiandong","family":"Bai","sequence":"first","affiliation":[{"name":"Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China"}]},{"given":"Jiawei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Yujiao","family":"Song","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Shenzhen 518129, China"}]},{"given":"Ling","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Zhixiang","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China"}]},{"given":"Ronghua","family":"Du","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1173-6593","authenticated-orcid":false,"given":"Haifeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","first-page":"18","article-title":"Dynamic Modeling of Urban Transportation Networks and Analysis of Its Travel Behaviors","volume":"2","author":"Huang","year":"2005","journal-title":"Chin. 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