{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:49:12Z","timestamp":1771267752652,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. ITS Res."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s13177-023-00362-4","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T09:01:56Z","timestamp":1690794116000},"page":"506-522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Intelligent Traffic Prediction by Combining Weather and Road Traffic Condition Information: A Deep Learning-Based Approach"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0896-0650","authenticated-orcid":false,"given":"Pushpendu","family":"Kar","sequence":"first","affiliation":[]},{"given":"Shuxin","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"issue":"3","key":"362_CR1","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCOM.2017.1600238CM","volume":"55","author":"H Menouar","year":"2017","unstructured":"Menouar, H., et al.: Uav-enabled intelligent transportation systems for the smart city: Applications and challenges. IEEE Communications Magazine 55(3), 22\u201328 (2017)","journal-title":"IEEE Communications Magazine"},{"key":"362_CR2","doi-asserted-by":"crossref","unstructured":"Oku, H. Two simple requirements for deterrence of traffic jam and its verification and practical use. SAE International Journal of Advances and Current Practices in Mobility 1(2019-01-1437), 485\u2013498 (2019)","DOI":"10.4271\/2019-01-1437"},{"key":"362_CR3","unstructured":"Jiang, Y., Kang, R., Li, D., Guo, S. & Havlin, S. Spatio-temporal propagation of traffic jams in urban traffic networks. arXiv:1705.08269 (2017)"},{"key":"362_CR4","unstructured":"Shenzhen Institute of Transportation Statistics. data set. [Online]. note https:\/\/opendata.sz.gov.cn\/data\/dataSet\/toDataDetails\/29200_00403589"},{"key":"362_CR5","unstructured":"Shenzhen Institute of Weather Statistics. weather data. [Online]. https:\/\/tianqi.2345.com\/wea_history\/59493.htm"},{"issue":"12","key":"362_CR6","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.3390\/s18124275","volume":"18","author":"D Zhu","year":"2018","unstructured":"Zhu, D., Du, H., Sun, Y., Cao, N.: Research on path planning model based on short-term traffic flow prediction in intelligent transportation system. Sensors 18(12), 4275 (2018)","journal-title":"Sensors"},{"key":"362_CR7","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","volume":"90","author":"Y Wu","year":"2018","unstructured":"Wu, Y., Tan, H., Qin, L., Ran, B., Jiang, Z.: A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies 90, 166\u2013180 (2018)","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"362_CR8","doi-asserted-by":"crossref","unstructured":"Roberts, S., Bonenberg, L., Meng, X., Moore, T. & Hill, C. Predictive intelligence for a rail traffic management system, 2117\u20132125 (2017)","DOI":"10.33012\/2017.15328"},{"key":"362_CR9","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhang, J.: Applying artificial intelligence and deep belief network to predict traffic congestion evacuation performance in smart cities. Applied Soft Computing 121,(2022) 108692","DOI":"10.1016\/j.asoc.2022.108692"},{"key":"362_CR10","doi-asserted-by":"crossref","unstructured":"Jia, Y., Wu, J. & Xu, M. Traffic flow prediction with rainfall impact using a deep learning method. Journal of advanced transportation 2017 (2017)","DOI":"10.1155\/2017\/6575947"},{"key":"362_CR11","doi-asserted-by":"crossref","unstructured":"Abdi, J., Moshiri, B., Abdulhai, B., Sedigh, A.K.: Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning. Neural Computing and Applications 23(1), 141\u2013159 (2013)","DOI":"10.1007\/s00521-012-0977-3"},{"issue":"7","key":"362_CR12","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1049\/iet-its.2017.0313","volume":"12","author":"D Zhang","year":"2018","unstructured":"Zhang, D., Kabuka, M.R.: Combining weather condition data to predict traffic flow: a gru-based deep learning approach. IET Intelligent Transport Systems 12(7), 578\u2013585 (2018)","journal-title":"IET Intelligent Transport Systems"},{"key":"362_CR13","doi-asserted-by":"crossref","unstructured":"Feng, M., Mao, S., Jiang, T.: Base station on-off switching in 5g wireless networks: Approaches and challenges. IEEE Wireless Communications 24(4), 46\u201354 (2017)","DOI":"10.1109\/MWC.2017.1600353"},{"key":"362_CR14","unstructured":"Chen, L., et al.: Forecast study of regional transportation carbon emissions based on svr. Journal of Transportation Systems Engineering and Information Technology 18(2), 13\u201319 (2018)"},{"issue":"4","key":"362_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3390\/a13040084","volume":"13","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Li, S., Li, X., Yao, Y.: Representation of traffic congestion data for urban road traffic networks based on pooling operations. Algorithms 13(4), 84 (2020)","journal-title":"Algorithms"},{"key":"362_CR16","doi-asserted-by":"publisher","first-page":"22686","DOI":"10.1109\/ACCESS.2020.2970250","volume":"8","author":"L Cai","year":"2020","unstructured":"Cai, L., et al.: A sample-rebalanced outlier-rejected $$ k $$-nearest neighbor regression model for short-term traffic flow forecasting. IEEE access 8, 22686\u201322696 (2020)","journal-title":"IEEE access"},{"key":"362_CR17","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1016\/j.proeng.2017.04.417","volume":"187","author":"SV Kumar","year":"2017","unstructured":"Kumar, S.V.: Traffic flow prediction using kalman filtering technique. Procedia Engineering 187, 582\u2013587 (2017)","journal-title":"Procedia Engineering"},{"key":"362_CR18","doi-asserted-by":"crossref","unstructured":"Wu, D., Ren, H., Su, G. & Guo, G. Millimeter-wave distance-dependent and height-dependent path loss models for 5G wireless systems, 1\u20135 (2017)","DOI":"10.1109\/ICCChina.2017.8330317"},{"issue":"1","key":"362_CR19","doi-asserted-by":"publisher","first-page":"97","DOI":"10.32604\/cmes.2022.016632","volume":"130","author":"P Chen","year":"2022","unstructured":"Chen, P., Zhang, W., Xiao, Z., Tian, Y.: Traffic accident detection based on deformable frustum proposal and adaptive space segmentation. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES 130(1), 97\u2013109 (2022)","journal-title":"CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES"},{"issue":"2","key":"362_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1049\/iet-its.2017.0144","volume":"12","author":"J Guo","year":"2018","unstructured":"Guo, J., Liu, Z., Huang, W., Wei, Y., Cao, J.: Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals. IET Intelligent Transport Systems 12(2), 143\u2013150 (2018)","journal-title":"IET Intelligent Transport Systems"},{"issue":"7","key":"362_CR21","first-page":"186","volume":"5","author":"SP Kailasam","year":"2016","unstructured":"Kailasam, S.P., Aruna, K., Sathik, M., et al.: Traffic flow prediction with big data using saes algorithm. JCSMC 5(7), 186\u2013193 (2016)","journal-title":"JCSMC"},{"key":"362_CR22","unstructured":"Shenzhen Municipal Meteorological Bureau of China. Shenzhen weather data. [Online]. http:\/\/weather.sz.gov.cn\/qixiangfuwu\/qixiangjiance\/weixingyuntu\/mindex.html"},{"key":"362_CR23","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.rser.2019.04.002","volume":"108","author":"HS Dhiman","year":"2019","unstructured":"Dhiman, H.S., Deb, D., Guerrero, J.M.: Hybrid machine intelligent svr variants for wind forecasting and ramp events. Renewable and Sustainable Energy Reviews 108, 369\u2013379 (2019)","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"1","key":"362_CR24","first-page":"41","volume":"15","author":"S Huang","year":"2018","unstructured":"Huang, S., et al.: Applications of support vector machine (svm) learning in cancer genomics. Cancer genomics & proteomics 15(1), 41\u201351 (2018)","journal-title":"Cancer genomics & proteomics"},{"key":"362_CR25","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patrec.2016.08.013","volume":"84","author":"M Ring","year":"2016","unstructured":"Ring, M., Eskofier, B.M.: An approximation of the gaussian rbf kernel for efficient classification with svms. Pattern Recognition Letters 84, 107\u2013113 (2016)","journal-title":"Pattern Recognition Letters"},{"key":"362_CR26","doi-asserted-by":"crossref","unstructured":"Probst, P., Wright, M.N., Boulesteix, A.-L.: Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: data mining and knowledge discovery 9(3), e1301 (2019)","DOI":"10.1002\/widm.1301"}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-023-00362-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-023-00362-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-023-00362-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T07:16:19Z","timestamp":1700032579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-023-00362-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["362"],"URL":"https:\/\/doi.org\/10.1007\/s13177-023-00362-4","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"value":"1348-8503","type":"print"},{"value":"1868-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,31]]},"assertion":[{"value":"20 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/Competing interests"}}]}}