{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T07:36:32Z","timestamp":1773473792180,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Fujian Province","award":["2022J011248"],"award-info":[{"award-number":["2022J011248"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2021J011198"],"award-info":[{"award-number":["2021J011198"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["RCS2021K003"],"award-info":[{"award-number":["RCS2021K003"]}]},{"name":"State Key Laboratory of Rail Traffic Control and Safety","award":["2022J011248"],"award-info":[{"award-number":["2022J011248"]}]},{"name":"State Key Laboratory of Rail Traffic Control and Safety","award":["2021J011198"],"award-info":[{"award-number":["2021J011198"]}]},{"name":"State Key Laboratory of Rail Traffic Control and Safety","award":["RCS2021K003"],"award-info":[{"award-number":["RCS2021K003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic state data are key to the proper operation of intelligent transportation systems (ITS). However, traffic detectors often receive environmental factors that cause missing values in the collected traffic state data. Therefore, aiming at the above problem, a method for imputing missing traffic state data based on a Diffusion Convolutional Neural Network\u2013Generative Adversarial Network (DCNN-GAN) is proposed in this paper. The proposed method uses a graph embedding algorithm to construct a road network structure based on spatial correlation instead of the original road network structure; through the use of a GAN for confrontation training, it is possible to generate missing traffic state data based on the known data of the road network. In the generator, the spatiotemporal features of the reconstructed road network are extracted by the DCNN to realize the imputation. Two real traffic datasets were used to verify the effectiveness of this method, with the results of the proposed model proving better than those of the other models used for comparison.<\/jats:p>","DOI":"10.3390\/s23239601","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T05:28:21Z","timestamp":1701667701000},"page":"9601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Missing Traffic Data Imputation Method Based on a Diffusion Convolutional Neural Network\u2013Generative Adversarial Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Chenchen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemei","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103737","DOI":"10.1016\/j.trc.2022.103737","article-title":"Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns","volume":"141","author":"Nie","year":"2022","journal-title":"Transp. 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