{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:29:29Z","timestamp":1771334969385,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,10]],"date-time":"2017-05-10T00:00:00Z","timestamp":1494374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.<\/jats:p>","DOI":"10.3390\/sym9050070","type":"journal-article","created":{"date-parts":[[2017,5,10]],"date-time":"2017-05-10T12:04:20Z","timestamp":1494417860000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine"],"prefix":"10.3390","volume":"9","author":[{"given":"Qing","family":"Shen","sequence":"first","affiliation":[{"name":"University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Xiaojuan","family":"Ban","sequence":"additional","affiliation":[{"name":"University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Chong","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,10]]},"reference":[{"key":"ref_1","first-page":"921","article-title":"Formation Mechanism of Traffic Congestion in View of Spatio-temporal Agglomeration of Residents\u2019 Daily Activities: A Case Study of Guangzhou","volume":"32","author":"Gu","year":"2012","journal-title":"Sci. Geogr. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.sbspro.2014.07.227","article-title":"Study on Traffic Congestion Patterns of Large City in China Taking Beijing as an Example","volume":"138","author":"Wen","year":"2014","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, R., Hu, W.P., Wang, H.L., and Wu, C. (2010, January 17\u201319). The Road Network Evolution Analysis of Guangzhou-Foshan Metropolitan Area Based on Kernel Density Estimation. Proceedings of the International Conference on Computational and Information Sciences, Chengdu, China.","DOI":"10.1109\/ICCIS.2010.83"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1109\/TITS.2012.2218237","article-title":"Efficient traffic state estimation for large-scale urban road networks","volume":"14","author":"Kong","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","unstructured":"Batool, F., and Khan, S.A. (2005, January 18). Traffic estimation and real time prediction using Ad Hoc networks. Proceedings of the IEEE Symposium on Emerging Technologies, Catania, Italy."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gomez, A.E., Alencar, F.A.R., Prado, P.V., Osorio, F.S., and Wolf, D.F. (2014, January 8\u201311). Traffic lights detection and state estimation using hidden markov models. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856486"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Purusothaman, S.B., and Parasuraman, K. (2013, January 25\u201326). Vehicular traffic density state estimation using Support Vector Machine. Proceedings of the 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), Tirunelveli, India.","DOI":"10.1109\/ICE-CCN.2013.6528610"},{"key":"ref_8","unstructured":"Huang, G.B., and Siew, C.K. (2004, January 6\u20139). Extreme learning machine: RBF network case. Proceedings of the 2004 Control, Automation, Robotics and Vision Conference (ICARCV), Kunming, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","article-title":"Letters: Convex incremental extreme learning machine","volume":"70","author":"Huang","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TSMCB.2008.2010506","article-title":"Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems","volume":"39","author":"Rong","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_11","first-page":"31","article-title":"Representational learning with extreme learning machine for big data","volume":"28","author":"Kasun","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1109\/TCYB.2015.2481713","article-title":"Multilayer Extreme Learning Machine with Subnetwork Nodes for Representation Learning","volume":"46","author":"Yang","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.patcog.2012.06.007","article-title":"An efficient illumination invariant face recognition framework via illumination enhancement and DD-DTCWT filtering","volume":"46","author":"Baradarani","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_14","first-page":"1","article-title":"Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction","volume":"99","author":"Yang","year":"2016","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1016\/j.neucom.2010.11.034","article-title":"GPU-accelerated and parallelized ELM ensembles for large-scale regression","volume":"74","author":"Heeswijk","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2011.09.015","article-title":"Voting based extreme learning machine","volume":"185","author":"Cao","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1109\/TNNLS.2013.2281839","article-title":"Sparse Bayesian Extreme Learning Machine for Multi-classification","volume":"25","author":"Luo","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TCYB.2014.2298235","article-title":"Sparse extreme learning machine for classification","volume":"44","author":"Bai","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","first-page":"1","article-title":"Extreme learning machines on high dimensional and large data applications: A survey","volume":"2015","author":"Cao","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_20","first-page":"1","article-title":"Protein sequence classification with improved extreme learning machine algorithms","volume":"2014","author":"Cao","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","article-title":"Semi-supervised and unsupervised extreme learning machines","volume":"44","author":"Huang","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, H., Qin, J., Sun, F., and Guo, D. (2016). Extreme Kernel Sparse Learning for Tactile Object Recognition. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2016.2614809"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shen, Q., Ban, X., Guo, C., and Wang, C. (2016, January 24\u201327). Kernel Semi-supervised Extreme Learning Machine Applied in Urban Traffic Congestion Evaluation. Proceedings of the 13th International Conference on Cooperative Design, Visualization, and Engineering (CDVE), Sydney, Australia.","DOI":"10.1007\/978-3-319-46771-9_12"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_26","unstructured":"Joachims, T. (1999, January 27\u201330). Transductive inference for text classification using support vector machines. Proceedings of the 6th International Conference on Machine Learning (ICML), Bled, Slovenia."},{"key":"ref_27","first-page":"1","article-title":"The ant system: Optimization by a colony of cooperation agents","volume":"26","author":"Drigo","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_28","first-page":"2399","article-title":"Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/5\/70\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:35:20Z","timestamp":1760207720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/5\/70"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,10]]},"references-count":28,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2017,5]]}},"alternative-id":["sym9050070"],"URL":"https:\/\/doi.org\/10.3390\/sym9050070","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,10]]}}}