{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T03:27:33Z","timestamp":1781234853281,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB1600600"],"award-info":[{"award-number":["2018YFB1600600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city\u2019s central area was given.<\/jats:p>","DOI":"10.3390\/s20185039","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T12:20:06Z","timestamp":1599222006000},"page":"5039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Novel Method about the Representation and Discrimination of Traffic State"],"prefix":"10.3390","volume":"20","author":[{"given":"Junfeng","family":"Jiang","sequence":"first","affiliation":[{"name":"College of artificial intelligence, Wuhan Technology and Business University, Wuhan 430073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiushi","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-9886","authenticated-orcid":false,"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"Faculty of Technology, Policy and Management, Safety and Security Science Group (S3G), Delft University of Technology, 2628BX Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haobo","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Transportation Systems Center (ITSC), Wuhan University of Technology, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3141\/2058-20","article-title":"Real-World Carbon Dioxide Impacts of Traffic Congestion","volume":"2058","author":"Barth","year":"2008","journal-title":"Transp. Res. Rec."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yuan, Y. (2020, January 18\u201320). Application of Intelligent Technology in Urban Traffic Congestion. Proceedings of the 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China.","DOI":"10.1109\/ICCEA50009.2020.00157"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zahid, M., Chen, Y., Jamal, A., and Memon, M.Q. (2020). Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers. Sensors, 20.","DOI":"10.3390\/s20030685"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1177\/0361198118786824","article-title":"Bayesian Network for Freeway Traffic State Prediction","volume":"2672","author":"Park","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3141\/1855-06","article-title":"Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway","volume":"1855","author":"Nanthawichit","year":"2003","journal-title":"Transp. Res. Rec. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, B., Sun, J., Wang, W., Xu, Z., Tian, T., Wang, Y., and Wei, J. (2018, January 6\u20137). Real Time Detection of Traffic Signal Running State and Remote Alarm for Fault Information at Road Intersection. Proceedings of the 2018 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, UK.","DOI":"10.23919\/IConAC.2018.8748996"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112753","DOI":"10.1016\/j.eswa.2019.06.041","article-title":"A novel sparse representation model for pedestrian abnormal trajectory understanding","volume":"138","author":"Chen","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TITS.2016.2587814","article-title":"Vehicle Behavior Learning via Sparse Reconstruction with l2-lp Minimization and Trajectory Similarity","volume":"18","author":"Chen","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1109\/TITS.2017.2658664","article-title":"Real-Time Traffic State Estimation with Connected Vehicles","volume":"18","author":"Khan","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1049\/iet-its.2009.0053","article-title":"Real-time urban traffic monitoring with global positioning system-equipped vehicles","volume":"4","author":"Shi","year":"2010","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_11","first-page":"22","article-title":"Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes","volume":"2","author":"Tao","year":"2012","journal-title":"J. Transp. Technol."},{"key":"ref_12","first-page":"272","article-title":"The comprehensive measure model for urban traffic congestion based on value function","volume":"31","author":"Hu","year":"2015","journal-title":"J. Southeast Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.trc.2015.01.033","article-title":"Estimation of Flow and Density Using Probe Vehicles with Spacing Measurement Equipment","volume":"53","author":"Seo","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102682","DOI":"10.1016\/j.trc.2020.102682","article-title":"Spatiotemporal trajectory characteristic analysis for traffic state transition prediction near expressway merge bottleneck","volume":"117","author":"Wan","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.trc.2013.05.012","article-title":"Dynamic data-driven local traffic state estimation and prediction","volume":"34","author":"Antoniou","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.trb.2004.03.003","article-title":"Real-time freeway traffic state estimation based on extended Kalman filter: A general approach","volume":"39","author":"Wang","year":"2005","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_17","first-page":"1","article-title":"Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion","volume":"2017","author":"Wang","year":"2017","journal-title":"J. Sens."},{"key":"ref_18","first-page":"104","article-title":"Real-time road traffic state prediction based on kernel-KNN","volume":"16","author":"Xu","year":"2018","journal-title":"Transp. A Transp. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102660","DOI":"10.1016\/j.trc.2020.102660","article-title":"Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data","volume":"117","author":"Zhan","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.tra.2018.10.035","article-title":"Classifying the traffic state of urban expressways: A machine-learning approach","volume":"137","author":"Cheng","year":"2020","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TITS.2006.874712","article-title":"POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction","volume":"7","author":"Quek","year":"2006","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/TFUZZ.2002.1006433","article-title":"Classification and prediction of road traffic using application-specific fuzzy clustering","volume":"10","author":"Stutz","year":"2002","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_23","unstructured":"Thomas, K., and Dia, H. (2020, August 06). A Neural Network Model for Arterial Incident Detection Using Probe Vehicle and Loop Detector Data. Available online: https:\/\/www.researchgate.net\/publication\/43483712_A_neural_network_model_for_arterial_incident_detection_using_probe_vehicle_and_loop_detector_data."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102635","DOI":"10.1016\/j.trc.2020.102635","article-title":"GE-GAN: A novel deep learning framework for road traffic state estimation","volume":"117","author":"Xu","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","unstructured":"Li, J. (2018). Real-Time Road Traffic State Prediction Based on SVM and Kalman Filter. Wireless Sensor Networks, Springer-Verlag Singapore Pte Ltd."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Min, Z., Yanlei, L., Dihua, S., and Senlin, C. (2017, January 28\u201330). Highway Traffic Abnormal State Detection Based on PCA-GA-SVM Algorithm. Proceedings of the 2017 29th Chinese Control and Decision Conference, Chongqing, China.","DOI":"10.1109\/CCDC.2017.7978993"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.ssci.2019.07.019","article-title":"Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships\u2019 maneuvering decisions using grey and fuzzy theories","volume":"120","author":"Xue","year":"2019","journal-title":"Saf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.eswa.2018.07.044","article-title":"Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees","volume":"115","author":"Xue","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.trc.2007.02.001","article-title":"A fuzzy-based system for incident detection in urban street networks","volume":"15","author":"Hawas","year":"2007","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0968-090X(03)00020-2","article-title":"Incident detection using support vector machines","volume":"11","author":"Yuan","year":"2003","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0968-090X(13)80001-0","article-title":"Simulation of Freeway Incident Detection Using Artificial Neural Networks","volume":"1","author":"Ritchie","year":"1993","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1109\/ACCESS.2019.2959125","article-title":"Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation","volume":"8","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.trc.2014.02.014","article-title":"Flow rate and time mean speed predictions for the urban freeway network using state space models","volume":"43","author":"Dong","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_34","unstructured":"Patil, G.R., Mathew, T.V., and Rao, K.V.K. (2014, January 10\u201312). Application of data mining techniques for traffic density estimation and prediction. Proceedings of the International Conference on Transportation Planning and Implementation Methodologies for Developing Countries, Mumbai, India."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.12815\/kits.2016.15.3.034","article-title":"Comparison of the Methodologies for Calculating Expressway Space Mean Speed Using Vehicular Trajectory Information from a Radar Detector","volume":"15","author":"Han","year":"2016","journal-title":"J. Korea Inst. Intell. Transp. Syst."},{"key":"ref_36","unstructured":"Yu, Y., and Trouv\u00e9, A. (2002, January 26\u201330). A non-linear K-means algorithm and its application to unsupervised clustering. Proceedings of the 6th International Conference on Signal Processing 2002 ICOSP-02, Beijing, China."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/72.363450","article-title":"Fuzzy multi-layer perceptron, inferencing and rule generation","volume":"6","author":"Mitra","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_38","first-page":"1","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Stephens, C.R., and Sukumar, R. (2006). An Introduction to Data Mining, Addison-Wesley.","DOI":"10.4135\/9781412973380.n22"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5039\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:56Z","timestamp":1760177216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":40,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185039"],"URL":"https:\/\/doi.org\/10.3390\/s20185039","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,4]]}}}