{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:59:09Z","timestamp":1775080749639,"version":"3.50.1"},"reference-count":41,"publisher":"Informa UK Limited","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"the National Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Central Universities and the Jiangsu Province University Graduate Student Research and Innovation Program","award":["KYLX_"],"award-info":[{"award-number":["KYLX_"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent Transportation Systems"],"published-print":{"date-parts":[[2021,5,4]]},"DOI":"10.1080\/15472450.2019.1617141","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T03:43:41Z","timestamp":1558669421000},"page":"313-329","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":15,"title":["Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information"],"prefix":"10.1080","volume":"25","author":[{"given":"Kun","family":"Tang","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China"},{"name":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China"},{"name":"School of Transportation, Southeast University, Nanjing, China"}]},{"given":"Shuyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"}]},{"given":"Aemal J.","family":"Khattak","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Nebraska Transportation Center, University of Nebraska-Lincoln, Lincoln, NE, USA"}]},{"given":"Yingjiu","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing, China"}]}],"member":"301","published-online":{"date-parts":[[2019,5,23]]},"reference":[{"key":"CIT0001","doi-asserted-by":"publisher","DOI":"10.3141\/2554-07"},{"issue":"1","key":"CIT0002","first-page":"153","volume":"19","author":"Bengio Y.","year":"2007","journal-title":"Advances in Neural Information Processing Systems"},{"key":"CIT0003","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2017.1408013"},{"key":"CIT0004","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2018.1542304"},{"key":"CIT0005","unstructured":"Cui, Z., Henrickson, K., Ke, R. & Wang, Y. (2018). High-order graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. arXiv preprint arXiv:1802.07007, (February)."},{"key":"CIT0006","unstructured":"Cui, Z., Ke, R. & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. arXiv Preprint arXiv:1801.02143."},{"key":"CIT0007","first-page":"1","author":"Delhome R.","year":"2017","journal-title":"Journal of Intelligent Transportation Systems"},{"key":"CIT0008","doi-asserted-by":"crossref","unstructured":"Duan, Y., Lv, Y. & Wang, F.Y. (2016). Travel time prediction with LSTM neural network. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.\u00a01053\u20131058.","DOI":"10.1109\/ITSC.2016.7795686"},{"key":"CIT0009","first-page":"1","author":"Fan S. K. S.","year":"2017","journal-title":"Soft Computing"},{"key":"CIT0010","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.08.017"},{"key":"CIT0011","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"CIT0012","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2012.03.006"},{"key":"CIT0013","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"CIT0014","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2013.03.008"},{"key":"CIT0015","unstructured":"Ke, R., Li, W., Cui, Z. & Wang, Y. H. (2018). Multi-lane traffic pattern learning and forecasting using convolutional neural network. In COTA International Symposium on Emerging Trend in Transportation."},{"key":"CIT0016","first-page":"1","author":"Kumar B. A.","year":"2017","journal-title":"Journal of Intelligent Transportation Systems"},{"issue":"2","key":"CIT0017","first-page":"865","volume":"16","author":"Lv Y.","year":"2015","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"CIT0018","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0119044"},{"key":"CIT0019","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.11.008"},{"key":"CIT0020","doi-asserted-by":"crossref","unstructured":"Nikovski, D., Nishiuma, N., Goto, Y. & Kumazawa, H. (2005). Univariate short-term prediction of road travel times. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp.\u00a01074\u20131079.","DOI":"10.1109\/ITSC.2005.1520200"},{"key":"CIT0021","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2016.1149700"},{"key":"CIT0022","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.01.015"},{"key":"CIT0023","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2016.1154764"},{"key":"CIT0024","unstructured":"Siripanpornchana, C., Panichpapiboon, S. & Chaovalit, P. (2017). Travel-time prediction with deep learning. IEEE Region 10 Annual International Conference, Proceedings\/TENCON, pp.\u00a01859\u20131862."},{"key":"CIT0025","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.02.033"},{"key":"CIT0026","doi-asserted-by":"publisher","DOI":"10.1177\/0361198118790330"},{"key":"CIT0027","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2879497"},{"key":"CIT0028","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.004"},{"key":"CIT0029","first-page":"22","author":"Van Lint H.","year":"2012","journal-title":"Artificial Intelligence Applications to Critical Transportation Issues"},{"key":"CIT0030","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y. & Manzagol, P.A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine learning \u2013 ICML \u201908, pp.\u00a01096\u20131103.","DOI":"10.1145\/1390156.1390294"},{"key":"CIT0031","first-page":"3371","volume":"11","author":"Vincent P.","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"CIT0032","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zheng, Y. & Xue, Y. (2014). Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD\u201914), (5), 25\u201334.","DOI":"10.1145\/2623330.2623656"},{"key":"CIT0033","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2016.01.004"},{"key":"CIT0034","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"CIT0035","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, C., Xie, X. & Sun, G. Z. (2010). An Interactive-Voting based Map Matching algorithm. In Proceedings \u2013 IEEE International Conference on Mobile Data Management, pp.\u00a043\u201352.","DOI":"10.1109\/MDM.2010.14"},{"key":"CIT0036","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.04.001"},{"key":"CIT0037","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2015.12.007"},{"key":"CIT0038","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2017.1412829"},{"key":"CIT0039","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2012.04.007"},{"key":"CIT0040","doi-asserted-by":"publisher","DOI":"10.1145\/2743025"},{"key":"CIT0041","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.03.049"}],"container-title":["Journal of Intelligent Transportation Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/15472450.2019.1617141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T02:59:10Z","timestamp":1618628350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/15472450.2019.1617141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,23]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5,4]]}},"alternative-id":["10.1080\/15472450.2019.1617141"],"URL":"https:\/\/doi.org\/10.1080\/15472450.2019.1617141","relation":{},"ISSN":["1547-2450","1547-2442"],"issn-type":[{"value":"1547-2450","type":"print"},{"value":"1547-2442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,23]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gits20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=gits20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2017-09-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-06","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}