{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:45:18Z","timestamp":1761709518950,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319992464"},{"type":"electronic","value":"9783319992471"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-99247-1_21","type":"book-chapter","created":{"date-parts":[[2018,8,10]],"date-time":"2018-08-10T10:26:21Z","timestamp":1533896781000},"page":"244-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["P-DBL: A Deep Traffic Flow Prediction Architecture Based on Trajectory Data"],"prefix":"10.1007","author":[{"given":"Jingyuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,11]]},"reference":[{"issue":"5","key":"21_CR1","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","volume":"15","author":"W Huang","year":"2014","unstructured":"Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191\u20132201 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"21_CR2","first-page":"653","volume":"16","author":"A Abadi","year":"2015","unstructured":"Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653\u2013662 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"21_CR3","unstructured":"Ahmed, M.S., Cook, A.R.: Analysis of Freeway Traffic Time-Series Data By Using Box-Jenkins Techniques (1979)"},{"issue":"1","key":"21_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3141\/1857-09","volume":"1857","author":"Y Kamarianakis","year":"2003","unstructured":"Kamarianakis, Y., Vouton, V.: Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp. Res. Rec. 1857(1), 74\u201384 (2003)","journal-title":"Transp. Res. Rec."},{"issue":"6","key":"21_CR5","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM Williams","year":"2003","unstructured":"Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664\u2013672 (2003)","journal-title":"J. Transp. Eng."},{"key":"21_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1007\/978-3-540-72393-6_121","volume-title":"Advances in Neural Networks \u2013 ISNN 2007","author":"X Jin","year":"2007","unstructured":"Jin, X., Zhang, Y., Yao, D.: Simultaneously prediction of network traffic flow based on PCA-SVR. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 1022\u20131031. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-72393-6_121"},{"key":"21_CR7","unstructured":"Leshem, G.: Traffic flow prediction using adaboost algorithm with random forests as a weak learner. In: Enformatika, p. 193 (2011)"},{"issue":"2","key":"21_CR8","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TITS.2011.2174051","volume":"13","author":"KY Chan","year":"2012","unstructured":"Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg-cmarquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13(2), 644\u2013654 (2012)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"21_CR9","first-page":"865","volume":"16","author":"Y Lv","year":"2015","unstructured":"Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865\u2013873 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: IEEE International Conference on Smart City\/SocialCom\/SustainCom, pp. 153\u2013158 (2015)","DOI":"10.1109\/SmartCity.2015.63"},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1016\/j.trpro.2017.05.180","volume":"25","author":"YQ Wang","year":"2017","unstructured":"Wang, Y.Q., Jing, L.: Study of rainfall impacts on freeway traffic flow characteristics. Transp. Res. Procedia 25, 1533\u20131543 (2017)","journal-title":"Transp. Res. Procedia"},{"key":"21_CR12","unstructured":"Agarwal, M., Maze, T.H., Souleyrette, R.: Impacts of weather on urban freeway traffic flow characteristics and facility capacity. In: Proceedings of the 2005 Mid-Continent Transportation Research Symposium, pp. 18\u201319 (2005)"},{"issue":"2","key":"21_CR13","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1002\/met.1348","volume":"21","author":"E Hooper","year":"2014","unstructured":"Hooper, E., Chapman, L., Quinn, A.: Investigating the impact of precipitation on vehicle speeds on uk motorways. Meteorol. Appl. 21(2), 194\u2013201 (2014)","journal-title":"Meteorol. Appl."},{"key":"21_CR14","unstructured":"Ibrahim, A.T., Hall, F.L.: Effect of adverse weather conditions on speed-flow-occupancy relationships (1994)"},{"issue":"8","key":"21_CR15","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735 (1997)","journal-title":"Neural Comput."},{"key":"21_CR16","unstructured":"Wu, Y., et al.: Bridging the gap between human and machine translation, Google\u2019s neural machine translation system (2016)"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"19","key":"21_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/chin.200419274","volume":"35","author":"DM Hawkins","year":"2004","unstructured":"Hawkins, D.M.: The problem of overfitting. Cheminform 35(19), 1 (2004)","journal-title":"Cheminform"},{"issue":"4","key":"21_CR19","first-page":"212","volume":"3","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212\u2013223 (2012)","journal-title":"Comput. Sci."},{"issue":"1","key":"21_CR20","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-99247-1_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:36:55Z","timestamp":1710232615000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-99247-1_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319992464","9783319992471"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-99247-1_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"11 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changchun","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ksem2018.venue.link\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"262","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"We have 3 reviews for 235 submissions, 4 reviews for 25 submissions and 5 review for 2 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}