{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:31:13Z","timestamp":1743057073841,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811533075"},{"type":"electronic","value":"9789811533082"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","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":[[2020]]},"DOI":"10.1007\/978-981-15-3308-2_43","type":"book-chapter","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T13:03:07Z","timestamp":1584018187000},"page":"399-406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PLSTM: Long Short-Term Memory Neural Networks for Propagatable Traffic Congested States Prediction"],"prefix":"10.1007","author":[{"given":"Yuxin","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lyuchao","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"issue":"5","key":"43_CR1","first-page":"956","volume":"43","author":"L Liao","year":"2015","unstructured":"Liao, L., Jiang, X., Zou, F., et al.: A spectral clustering method for big trajectory data mining with latent semantic correlation. Chin. J. Electron. 43(5), 956\u2013964 (2015)","journal-title":"Chin. J. Electron."},{"issue":"3","key":"43_CR2","first-page":"815","volume":"19","author":"L Liao","year":"2018","unstructured":"Liao, L., Wu, J., Zou, F., et al.: Trajectory topic modelling to characterize driving behaviors with GPS-based trajectory data. J. Internet Technol. 19(3), 815\u2013824 (2018)","journal-title":"J. Internet Technol."},{"issue":"1","key":"43_CR3","first-page":"e1285","volume":"9","author":"LNN Do","year":"2018","unstructured":"Do, L.N.N., Taherifar, N., Vu, H.L.: Survey of neural network-based models for short-term traffic state prediction. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(1), e1285 (2018)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"issue":"11","key":"43_CR4","doi-asserted-by":"publisher","first-page":"3550","DOI":"10.1109\/TITS.2018.2835523","volume":"19","author":"M Chen","year":"2018","unstructured":"Chen, M., Yu, X., Liu, Y.: PCNN: deep convolutional networks for short-term traffic congestion prediction. IEEE Trans. Intell. Transp. Syst. 19(11), 3550\u20133559 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"43_CR5","doi-asserted-by":"publisher","first-page":"69481","DOI":"10.1109\/ACCESS.2018.2881039","volume":"6","author":"Z Chen","year":"2018","unstructured":"Chen, Z., Yang, Y., Huang, L., et al.: Discovering urban traffic congestion propagation patterns with taxi trajectory data. IEEE Access 6, 69481\u201369491 (2018)","journal-title":"IEEE Access"},{"issue":"2","key":"43_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/TBDATA.2016.2587669","volume":"3","author":"H Nguyen","year":"2017","unstructured":"Nguyen, H., Liu, W., Chen, F.: Discovering congestion propagation patterns in spatio-temporal traffic data. IEEE Trans. Big Data 3(2), 169\u2013180 (2017)","journal-title":"IEEE Trans. Big Data"},{"key":"43_CR7","doi-asserted-by":"crossref","unstructured":"Chen, C., Hu, J., Meng, Q., et al. (eds.): Short-time traffic flow prediction with ARIMA-GARCH model. In: 2011 IEEE Intelligent Vehicles Symposium (IV) (2011)","DOI":"10.1109\/IVS.2011.5940418"},{"issue":"1","key":"43_CR8","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/43.974139","volume":"21","author":"X Yang","year":"2002","unstructured":"Yang, X., Kastner, R., Sarrafzadeh, M., et al.: Congestion estimation during top-down placement. IEEE Trans. Comput. Aided Des. Integr. Circuits 21(1), 72\u201380 (2002)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits"},{"issue":"1","key":"43_CR9","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3141\/2595-12","volume":"2595","author":"J Kim","year":"2016","unstructured":"Kim, J., Wang, G.: Diagnosis and prediction of traffic congestion on urban road networks using Bayesian networks. Transp. Res. Rec. 2595(1), 108\u2013118 (2016)","journal-title":"Transp. Res. Rec."},{"key":"43_CR10","unstructured":"Nguyen, H.N., Krishnakumari, P., Vu, H.L., et al. (eds.): Traffic congestion pattern classification using multi-class SVM. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (2016)"},{"issue":"2","key":"43_CR11","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","volume":"11","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Chen, W., Wu, X., et al.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68\u201375 (2017)","journal-title":"IET Intell. Transp. Syst."},{"key":"43_CR12","doi-asserted-by":"crossref","unstructured":"Wang, J., Hu, F., Li, L. (eds.): Deep bi-directional long short-term memory model for short-term traffic flow prediction. In: International Conference on Neural Information Processing (2017)","DOI":"10.1007\/978-3-319-70139-4_31"},{"key":"43_CR13","unstructured":"Hermans, M., Schrauwen, B. (eds.): Training and analysing deep recurrent neural networks. In: Advances in Neural Information Processing Systems (2013)"},{"key":"43_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint (2014)"},{"issue":"8","key":"43_CR15","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Advances in Intelligent Systems and Computing","Genetic and Evolutionary Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3308-2_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T21:20:20Z","timestamp":1584048020000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3308-2_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811533075","9789811533082"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3308-2_43","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICGEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Genetic and Evolutionary Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qingdao","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icgec2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bit.kuas.edu.tw\/~icgec19\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}