{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:25:54Z","timestamp":1753885554548,"version":"3.41.2"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":242,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51908018"],"award-info":[{"award-number":["51908018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Short\u2010term traffic prediction under corrupted or missing data for large\u2010scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short\u2010term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large\u2010scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short\u2010term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.<\/jats:p>","DOI":"10.1155\/2021\/9966382","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:20:11Z","timestamp":1630448411000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Short\u2010Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6389-6524","authenticated-orcid":false,"given":"Tian","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7082-9231","authenticated-orcid":false,"given":"Guanghong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3504-8963","authenticated-orcid":false,"given":"Yilong","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.05.012"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18072287"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.25046\/aj020387"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.10.002"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.4.1.42"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.3141\/2061-08"},{"key":"e_1_2_9_9_2","article-title":"Short-term traffic and travel time prediction models","volume":"173","author":"Van Lint H.","year":"2012","journal-title":"Transportation Research E-Circular"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1955.0089"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.3141\/1802-28"},{"key":"e_1_2_9_13_2","unstructured":"Ben-AkivaM. 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