{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T18:12:24Z","timestamp":1725905544179},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319633145"},{"type":"electronic","value":"9783319633152"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-63315-2_65","type":"book-chapter","created":{"date-parts":[[2017,7,20]],"date-time":"2017-07-20T04:48:34Z","timestamp":1500526114000},"page":"747-758","source":"Crossref","is-referenced-by-count":3,"title":["A Classification and Predication Framework for Taxi-Hailing Based on Big Data"],"prefix":"10.1007","author":[{"given":"Changqing","family":"Yin","sequence":"first","affiliation":[]},{"given":"Yiwei","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,7,21]]},"reference":[{"key":"65_CR1","unstructured":"de Br\u00e9bisson, A., Simon, \u00c9., Auvolat, A., Vincent, P., Bengio, Y.: Artificial neural networks applied to taxi destination prediction. https:\/\/arxiv.org\/abs\/1508.00021"},{"key":"65_CR2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.knosys.2016.12.005","volume":"119","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Wang, L., Jia, L., Li, F., Zhang, L., Zhao, M.: Projective label propagation by label embedding: a deep label prediction framework for representation and classification. Knowl.-Based Syst. 119, 94\u2013112 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"65_CR3","doi-asserted-by":"crossref","unstructured":"Shi, A., Weiming, K.: Prediction of urban traffic abnormity based on causal network. In: 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), pp. 574\u2013577 (2015)","DOI":"10.1109\/ISDEA.2015.147"},{"key":"65_CR4","unstructured":"M\u00fcnz, G., Li, S., Carle, G.: Traffic anomaly detection using k-means clustering. In: GI\/ITG Workshop MMBnet (2007)"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"Xuewu, Z., Yongjun, L.: The city taxi quantity prediction via GM-BP model. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1594\u20131598 (2015)","DOI":"10.1109\/CYBER.2015.7288183"},{"key":"65_CR6","doi-asserted-by":"crossref","unstructured":"Manasseh, C., Sengupta, R.: Predicting driver destination using machine learning techniques. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 142\u2013147 (2013)","DOI":"10.1109\/ITSC.2013.6728224"},{"key":"65_CR7","doi-asserted-by":"crossref","unstructured":"Ahmad, B.I., Murphy, J.K., Godsill, S., Langdon, P.M., Hardy, R.: Intelligent interactive displays in vehicles with intent prediction: a Bayesian framework. IEEE Sig. Process. Mag. 34(2), 82\u201394 (2017)","DOI":"10.1109\/MSP.2016.2638699"},{"key":"65_CR8","doi-asserted-by":"crossref","unstructured":"Tak, S., Kim, S., Oh, S., Yeo, H.: Development of a data-driven framework for real-time travel time prediction. Comput.-Aided Civil Infrastruct. Eng. 31(10), 777\u2013793 (2016)","DOI":"10.1111\/mice.12205"},{"key":"65_CR9","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.is.2016.01.007","volume":"64","author":"T Liebig","year":"2017","unstructured":"Liebig, T., Piatkowski, N., Bockermann, C., Morik, K.: Dynamic route planning with real-time traffic predictions. Inf. Syst. 64, 258\u2013265 (2017)","journal-title":"Inf. Syst."},{"key":"65_CR10","doi-asserted-by":"crossref","unstructured":"Hu, W., Yan, L., Wang, H., Du, B., Tao, D.: Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model. Inf. Sci. 381, 209\u2013228 (2017)","DOI":"10.1016\/j.ins.2016.11.023"},{"key":"65_CR11","doi-asserted-by":"crossref","unstructured":"Oliveira, T.P., Barbar, J.S., Soares, A.S.: Computer network traffic prediction: a comparison between traditional and deep learning neural networks. IJBDI 3(1), 28\u201337 (2016)","DOI":"10.1504\/IJBDI.2016.073903"},{"issue":"7","key":"65_CR12","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1007\/s00521-015-1991-z","volume":"27","author":"H Li","year":"2016","unstructured":"Li, H.: Research on prediction of traffic flow based on dynamic fuzzy neural networks. Neural Comput. Appl. 27(7), 1969\u20131980 (2016)","journal-title":"Neural Comput. Appl."},{"key":"65_CR13","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.eswa.2016.06.032","volume":"62","author":"A Bezuglov","year":"2016","unstructured":"Bezuglov, A., Comert, G.: Short-term freeway traffic parameter prediction: application of grey system theory models. Expert Syst. Appl. 62, 284\u2013292 (2016)","journal-title":"Expert Syst. Appl."},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Li, J., Mei, X., Prokhorov, D., Tao, D.: Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 690\u2013703 (2017)","DOI":"10.1109\/TNNLS.2016.2522428"},{"key":"65_CR15","unstructured":"Yi, H., Jung, H., Bae, S.: Deep neural networks for traffic flow prediction. In: BigComp, pp. 328\u2013331 (2017)"},{"key":"65_CR16","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction. CoRR abs\/1701.04245 (2017)","DOI":"10.3390\/s17040818"},{"key":"65_CR17","doi-asserted-by":"crossref","unstructured":"Mitrovic, N., Asif, M.T., Dauwels, J., Jaillet, P.: Low-dimensional models for compressed sensing and prediction of large-scale traffic data. IEEE Trans. Intell. Transp. Syst. 16(5), 2949\u20132954 (2015)","DOI":"10.1109\/TITS.2015.2411675"},{"key":"65_CR18","doi-asserted-by":"crossref","unstructured":"Park, J., Li, D., Murphey, Y.L.: Real time vehicle speed prediction using a neural network traffic model. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2991\u20132996 (2011)","DOI":"10.1109\/IJCNN.2011.6033614"},{"key":"65_CR19","unstructured":"Luo, Q.: On discovering regional taxi service disequilibrium with geographical collaborative filtering. In: 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS) (2014)"},{"issue":"1","key":"65_CR20","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/72.896803","volume":"12","author":"DF Akhmetov","year":"2001","unstructured":"Akhmetov, D.F., Dote, Y., Ovaska, S.J.: Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series. IEEE Trans. Neural Netw. 12(1), 148\u2013152 (2001)","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Methodologies"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-63315-2_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T23:13:41Z","timestamp":1569885221000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-63315-2_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319633145","9783319633152"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-63315-2_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}