{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:48:29Z","timestamp":1742982509355,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811076046"},{"type":"electronic","value":"9789811076053"}],"license":[{"start":{"date-parts":[[2017,12,20]],"date-time":"2017-12-20T00:00:00Z","timestamp":1513728000000},"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\/978-981-10-7605-3_220","type":"book-chapter","created":{"date-parts":[[2017,12,19]],"date-time":"2017-12-19T06:22:45Z","timestamp":1513664565000},"page":"1383-1390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Representation of Raw Traffic Data: An Embed-and-Aggregate Framework for High-Level Traffic Analysis"],"prefix":"10.1007","author":[{"given":"Woosung","family":"Choi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghyeon","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taemin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyeongseok","family":"Hyun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taehyung","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soonyoung","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,20]]},"reference":[{"key":"220_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32460-4","volume-title":"Traffic Flow Dynamics: Data, Models and Simulation","author":"M Treiber","year":"2013","unstructured":"Treiber, M., Kesting, A.: Traffic Flow Dynamics: Data, Models and Simulation. Springer, Heidelberg (2013)"},{"issue":"1","key":"220_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1068\/b38141","volume":"40","author":"S Gao","year":"2013","unstructured":"Gao, S., et al.: Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environ. Plann. B Plann. Des. 40(1), 135\u2013153 (2013)","journal-title":"Environ. Plann. B Plann. Des."},{"key":"220_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.sbspro.2013.08.076","volume":"96","author":"L Zhang","year":"2013","unstructured":"Zhang, L., Liu, Q., Yang, W., Wei, N., Dong, D.: An improved K-nearest neighbor model for short-term traffic flow prediction. Procedia Soc. Behav. Sci. 96, 653\u2013662 (2013)","journal-title":"Procedia Soc. Behav. Sci."},{"issue":"7","key":"220_CR4","doi-asserted-by":"publisher","first-page":"04014026","DOI":"10.1061\/(ASCE)TE.1943-5436.0000672","volume":"140","author":"S Wu","year":"2014","unstructured":"Wu, S., Yang, Z., Zhu, X., Yu., B.: Improved K-nn for short-term traffic forecasting using temporal and spatial information. J. Trans. Eng. 140(7), 04014026 (2014)","journal-title":"J. Trans. Eng."},{"key":"220_CR5","doi-asserted-by":"crossref","unstructured":"Tak, S., Kim, S., Jang, K., Yeo, H.: Real-time travel time prediction using multi-level K-nearest neighbor algorithm and data fusion method. In: Computing in Civil and Building Engineering, pp. 1861\u20131868 (2014)","DOI":"10.1061\/9780784413616.231"},{"issue":"2","key":"220_CR6","doi-asserted-by":"publisher","first-page":"158","DOI":"10.7470\/jkst.2016.34.2.158","volume":"34","author":"H Kim","year":"2016","unstructured":"Kim, H., Park, S.H., Jang, K.: Short-term traffic states prediction using K-nearest neighbor algorithm: focused on urban expressway. J. Korean Soc. Transp. 34(2), 158\u2013167 (2016)","journal-title":"J. Korean Soc. Transp."},{"key":"220_CR7","doi-asserted-by":"publisher","first-page":"04016018","DOI":"10.1061\/(ASCE)TE.1943-5436.0000816","volume":"142","author":"B Yu","year":"2016","unstructured":"Yu, B., Song, X., Guan, F., Yang, Z., Yao, B.: k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J. Transp. Eng. 142, 04016018 (2016)","journal-title":"J. Transp. Eng."},{"issue":"2","key":"220_CR8","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. (ITS) 16(2), 865\u2013873 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst. (ITS)"},{"key":"220_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"NG Polson","year":"2017","unstructured":"Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C-Emerg. Technol. (Transport. Res. C-Emerg.) 79, 1\u201317 (2017)","journal-title":"Transp. Res. Part C-Emerg. Technol. (Transport. Res. C-Emerg.)"},{"issue":"4","key":"220_CR10","doi-asserted-by":"publisher","first-page":"397","DOI":"10.3846\/16484142.2013.818057","volume":"30","author":"K Kumar","year":"2015","unstructured":"Kumar, K., Parida, M., Katiyar, V.K.: Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport 30(4), 397\u2013405 (2015)","journal-title":"Transport"},{"issue":"4","key":"220_CR11","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1109\/TITS.2013.2267735","volume":"14","author":"Y-S Jeong","year":"2013","unstructured":"Jeong, Y.-S., Byon, Y.-J., Castro-Neto, M.M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. (ITS) 14(4), 1700\u20131707 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst. (ITS)"},{"key":"220_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12544-014-0149-x","volume":"7","author":"S Vasantha Kumar","year":"2015","unstructured":"Vasantha Kumar, S., Vanajakshi, L.: Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur. Transp. Res. Rev. (ETRR) 7, 1\u20139 (2015)","journal-title":"Eur. Transp. Res. Rev. (ETRR)"},{"key":"220_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.future.2015.11.013","volume":"61","author":"X Konga","year":"2016","unstructured":"Konga, X., Xua, Z., Shenb, G., Wanga, J., Yanga, Q., Zhanga, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener. Comput. Syst. (FGCS) 61, 97\u2013107 (2016)","journal-title":"Future Gener. Comput. Syst. (FGCS)"},{"key":"220_CR14","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, 653\u2013662 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"220_CR15","unstructured":"Fu, Z., Hu, W., Tan, T.: Similarity based vehicle trajectory clustering and anomaly detection. In: 2005 IEEE International Conference on Image Processing, ICIP 2005 (2005)"},{"key":"220_CR16","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.trc.2016.08.006","volume":"71","author":"Z Zhang","year":"2016","unstructured":"Zhang, Z., He, Q., Tong, H., Gou, J., Li, X.: Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network. Transp. Res. Part C Emerg. Technol. 71, 284\u2013302 (2016)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"2","key":"220_CR17","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P.Y., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"220_CR18","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhang, C., Zhu, Z., Huang, J., Bi, J.: Trajectory clustering via deep representation learning. In: International Joint Conference on Neural Networks (IJCNN), (2017)","DOI":"10.1109\/IJCNN.2017.7966345"},{"key":"220_CR19","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 3104\u20133112 (2014)"},{"key":"220_CR20","doi-asserted-by":"crossref","unstructured":"Perronnin, F., Dance, C.R.: Fisher kernels on visual vocabularies for image categorization. In: CVPR (2007)","DOI":"10.1109\/CVPR.2007.383266"},{"key":"220_CR21","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C., P\u00e9rez, P.: Aggregating local descriptors into a compact image representation. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304\u20133311. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540039"},{"issue":"9","key":"220_CR22","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","volume":"34","author":"H. Jegou","year":"2012","unstructured":"Jegou, H., et al.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704\u20131716 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"220_CR23","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Zisserman, A.: All about VLAD. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1578\u20131585. IEEE, Piscataway (2013)","DOI":"10.1109\/CVPR.2013.207"},{"key":"220_CR24","doi-asserted-by":"publisher","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","volume":"34","author":"H J\u00e9gou","year":"2012","unstructured":"J\u00e9gou, H., Perronnin, F., Douze, M., S\u00e1nchez, J., P\u00e9rez, P., Schmid, C.: Aggregating local images descriptors into compact codes. IEEE PAMI 34, 1704\u20131716 (2012)","journal-title":"IEEE PAMI"},{"key":"220_CR25","doi-asserted-by":"crossref","unstructured":"Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the ICCV (2003)","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"220_CR26","unstructured":"KDD Cup 2017: Highway Tollgates Traffic Flow Prediction. \n                  http:\/\/www.kdd.org\/kdd2017\/announcements\/view\/announcing-kdd-cup-2017-highway-tollgates-traffic-flow-prediction"},{"key":"220_CR27","unstructured":"KDD CUP 2017. \n                  https:\/\/tianchi.aliyun.com\/competition\/introduction.htm?spm=5176.100068.5678.1.LTxGCd&raceId=231597"}],"container-title":["Lecture Notes in Electrical Engineering","Advances in Computer Science and Ubiquitous Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-10-7605-3_220","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T22:14:24Z","timestamp":1558131264000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-10-7605-3_220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,20]]},"ISBN":["9789811076046","9789811076053"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-10-7605-3_220","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2017,12,20]]},"assertion":[{"value":"20 December 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CUTE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Information Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taichung","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cute2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}