{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:07:59Z","timestamp":1774444079252,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91746116 and 51741101"],"award-info":[{"award-number":["91746116 and 51741101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Project of Guizhou Province","doi-asserted-by":"publisher","award":["[2018]5788, 407 Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02"],"award-info":[{"award-number":["[2018]5788, 407 Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.<\/jats:p>","DOI":"10.3390\/s19102229","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"2229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8155-5996","authenticated-orcid":false,"given":"Sen","family":"Zhang","sequence":"first","affiliation":[{"name":"Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Big Data, Guizhou Institute of Technology, Guiyang 550003, China"}]},{"given":"Yong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3893-1594","authenticated-orcid":false,"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Big Data Statistics, GuiZhou University of Finance and Economics, Guiyang 550025, China"}]},{"given":"Yong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-6000","authenticated-orcid":false,"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8725-6660","authenticated-orcid":false,"given":"Jianjun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550003, China"},{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57311","DOI":"10.1109\/ACCESS.2018.2873569","article-title":"Congestion Prediction With Big Data for Real-Time Highway Traffic","volume":"6","author":"Tseng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3550","DOI":"10.1109\/TITS.2018.2835523","article-title":"PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction","volume":"19","author":"Chen","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/0361198105191700117","article-title":"Freeway Detector Assessment: Aggregate Data from Remote Traffic Microwave Sensor","volume":"1917","author":"Coifman","year":"2005","journal-title":"Transp. Res. Record J. Transp. Res. Board"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fouladgar, M., Parchami, M., Elmasri, R., and Ghaderi, A. (2017, January 14\u201319). Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966128"},{"key":"ref_5","first-page":"865","article-title":"Traffic Flow Prediction with Big Data: A Deep Learning Approach","volume":"16","author":"Lv","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, H., Wu, Z., Wang, S., Wang, Y., and Ma, X. (2017). Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks. Sensors, 17.","DOI":"10.3390\/s17071501"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, X., Yu, H., Wang, Y., and Wang, Y. (2015). Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0119044"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.trc.2014.02.013","article-title":"Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction","volume":"43","author":"Zhang","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1109\/TITS.2015.2491365","article-title":"A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy","volume":"17","author":"Onieva","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","unstructured":"Ben-Akiva, M., Bierlaire, M., Koutsopoulos, H., and Mishalani, R. (1998). DynaMIT: A Simulation-Based System for Traffic Prediction, DACCORD Short Term Forecasting Workshop."},{"key":"ref_11","unstructured":"Dailey, D.J., and Trepanier, T. (2006). The Use of Weather Data to Predict Non-Recurring Traffic Congestion, Washington Department of Transportation. Technical Report."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lima, A.M., and Campos, J. (2016, January 1\u20134). Evaluating the Use of Traffic Information from Web Map Services to Analyze the Impact of Non-Recurrent Events. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795892"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ali, U., and Mahmood, T. (2017). Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review. First International Conference on Intelligent Transport Systems, Springer.","DOI":"10.1007\/978-3-319-93710-6_11"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3844\/jcssp.2011.949.953","article-title":"Highway Traffic Incident Detection Using High-Resolution Aerial Remote Sensing Imagery","volume":"7","author":"Kahaki","year":"2011","journal-title":"J. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kahaki, S.M.M., and Nordin, M.J. (2011, January 28\u201329). Vision-Based Automatic Incident Detection System Using Image Sequences for Intersections. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligence Robotics, Putrajaya, Malaysia.","DOI":"10.1109\/ICPAIR.2011.5976902"},{"key":"ref_16","unstructured":"(2018, October 07). Beijing Traffic Management Bureau, Available online: http:\/\/eye.bjjtw.gov.cn\/Web-T_bjjt_new\/Main.html."},{"key":"ref_17","unstructured":"(2018, June 23). WSDOT\u2014Seattle Washington Cameras. Available online: http:\/\/www.wsdot.com\/traffic\/seattle\/default.aspx."},{"key":"ref_18","unstructured":"(2018, October 06). Google Maps. Available online: https:\/\/www.google.com\/maps."},{"key":"ref_19","unstructured":"(2018, March 06). AutoNavi Map. Available online: https:\/\/ditu.amap.com."},{"key":"ref_20","unstructured":"(2018, October 06). Bing Maps. Available online: https:\/\/www.bing.com\/maps."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1080\/01441647.2018.1442887","article-title":"Spatiotemporal Traffic Forecasting: Review and Proposed Directions","volume":"38","author":"Ermagun","year":"2018","journal-title":"Transp. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_23","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_24","unstructured":"Krizhevsky, A., and Hinton, G.E. (2011). Using Very Deep Autoencoders for Content-Based Image Retrieval. 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Ciaco."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Feng, X., Zhang, Y., and Glass, J. (2014, January 4\u20139). Speech Feature Denoising and Dereverberation via Deep Autoencoders for Noisy Reverberant Speech Recognition. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6853900"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, D., Yang, Y., and Ning, S. (2018, January 8\u201313). DeepSTCL: A Deep Spatio-Temporal ConvLSTM for Travel Demand Prediction. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489530"},{"key":"ref_27","first-page":"119","article-title":"A Review of Travel Time Estimation and Forecasting for Advanced Traveller Information Systems","volume":"11","author":"Mori","year":"2015","journal-title":"Transp. A Transp. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0968-090X(02)00009-8","article-title":"Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting","volume":"10","author":"Smith","year":"2002","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","article-title":"Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results","volume":"129","author":"Williams","year":"2003","journal-title":"J. Transp. Eng."},{"key":"ref_30","unstructured":"Ghosh, B., Basu, B., and O\u2019Mahony, M. (2005, January 9). Time-Series Modelling for Forecasting Vehicular Traffic Flow in Dublin. Proceedings of the 84th Annual Meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_31","first-page":"43","article-title":"A Multiplicative Seasonal ARIMA\/GARCH Model in EVN Traffic Prediction","volume":"8","author":"Tran","year":"2015","journal-title":"Int. J. Commun. Netw. Syst. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","article-title":"Combining Kohonen Maps with Arima Time Series Models to Forecast Traffic Flow","volume":"4","author":"Dougherty","year":"1996","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3141\/1776-25","article-title":"Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling","volume":"1776","author":"Williams","year":"2001","journal-title":"Transp. Res. Record J. Transp. Res. Board"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1061\/(ASCE)0733-947X(2009)135:9(658)","article-title":"Multivariate Traffic Forecasting Technique Using Cell Transmission Model and SARIMA Model","volume":"135","author":"Szeto","year":"2009","journal-title":"J. Transp. Eng."},{"key":"ref_35","first-page":"22","article-title":"Short-Term Traffic and Travel Time Prediction Models","volume":"22","year":"2012","journal-title":"Artif. Intell. Appl. Crit. Transp. Issues"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-Term Traffic Forecasting: Where We Are and Where We\u2019re Going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1061\/(ASCE)0733-947X(1991)117:2(178)","article-title":"Nonparametric Regression and Short-Term Freeway Traffic Forecasting","volume":"117","author":"Davis","year":"1991","journal-title":"J. Transp. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1061\/(ASCE)0733-947X(2003)129:2(161)","article-title":"Traffic Prediction Using Multivariate Nonparametric Regression","volume":"129","author":"Clark","year":"2003","journal-title":"J. Transp. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1049\/iet-its.2011.0123","article-title":"Dynamic Near-Term Traffic Flow Prediction: Systemoriented Approach Based on Past Experiences","volume":"6","author":"Chang","year":"2012","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1061\/JHTRCQ.0000615","article-title":"Short-Term Traffic Flow Forecasting Based on Combination of K-Nearest Neighbor and Support Vector Regression","volume":"12","author":"Liu","year":"2018","journal-title":"J. Highway Transp. Res. Dev. (Engl. Ed.)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1049\/iet-its.2017.0059","article-title":"Feature Selection-Based Approach for Urban Short-Term Travel Speed Prediction","volume":"12","author":"Zheng","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TITS.2004.837813","article-title":"Travel-Time Prediction with Support Vector Regression","volume":"5","author":"Wu","year":"2004","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6164","DOI":"10.1016\/j.eswa.2008.07.069","article-title":"Online-SVR for Short-Term Traffic Flow Prediction under Typical and Atypical Traffic Conditions","volume":"36","author":"Jeong","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1016\/j.apm.2010.09.005","article-title":"Forecasting Urban Traffic Flow by SVR with Continuous ACO","volume":"35","author":"Hong","year":"2011","journal-title":"Appl. Math. Model."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.trc.2010.10.004","article-title":"Statistical Methods versus Neural Networks in Transportation Research: Differences, Similarities and Some Insights","volume":"19","author":"Karlaftis","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"397","DOI":"10.3846\/16484142.2013.818057","article-title":"Short Term Traffic Flow Prediction in Heterogeneous Condition Using Artificial Neural Network","volume":"30","author":"Kumar","year":"2015","journal-title":"Transport"},{"key":"ref_47","unstructured":"Kashi, S.O.M., and Akbarzadeh, M. (2018). A Framework for Short-Term Traffic Flow Forecasting Using the Combination of Wavelet Transformation and Artificial Neural Networks. J. Intell. Transp. Syst., 1\u201312."},{"key":"ref_48","first-page":"32","article-title":"High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality","volume":"1","author":"Donoho","year":"2000","journal-title":"AMS Math Chall. Lecture"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Verleysen, M., and Fran\u00e7ois, D. (2005). The Curse of Dimensionality in Data Mining and Time Series Prediction. International Work-Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/11494669_93"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","article-title":"Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning","volume":"15","author":"Huang","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Duan, Y., Lv, Y., and Wang, F.Y. (2016, January 10\u201312). Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times. Proceedings of the 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Beijing, China.","DOI":"10.1109\/SOLI.2016.7551691"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Pan, L. (2015, January 19\u201321). Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network. Proceedings of the 2015 IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity), Chengdu, China.","DOI":"10.1109\/SmartCity.2015.63"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_54","unstructured":"Chen, Y.y., Lv, Y., Li, Z., and Wang, F.Y. (2016, January 1\u20134). Long Short-Term Memory Model for Traffic Congestion Prediction with Online Open Data. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9508","DOI":"10.1109\/TVT.2016.2585575","article-title":"Improving Traffic Flow Prediction with Weather Information in Connected Cars: A Deep Learning Approach","volume":"65","author":"Koesdwiady","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Soua, R., Koesdwiady, A., and Karray, F. (2016, January 24\u201329). Big-Data-Generated Traffic Flow Prediction Using Deep Learning and Dempster-Shafer Theory. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727607"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.trb.2013.03.006","article-title":"Experienced travel time prediction for congested freeways","volume":"53","author":"Yildirimoglu","year":"2013","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lv, L., Chen, M., Liu, Y., and Yu, X. (2015). A plane moving average algorithm for short-term traffic flow prediction. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-319-18032-8_28"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.trc.2015.08.017","article-title":"Short-term traffic flow rate forecasting based on identifying similar traffic patterns","volume":"66","author":"Habtemichael","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/mice.12221","article-title":"Short-Term Traffic Speed Prediction for an Urban Corridor","volume":"32","author":"Yao","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lu, C., Hirsch, M., and Sch\u00f6lkopf, B. (2017, January 21\u201326). Flexible Spatio-Temporal Networks for Video Prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.230"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yan, X., Wang, Y., Yang, Z., and Wu, J. (2017). Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data. Sustainability, 9.","DOI":"10.3390\/su9040533"},{"key":"ref_64","unstructured":"(2018, June 09). Custom Colormaps. Available online: https:\/\/github.com\/CSlocumWX\/custom_colormap."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Tostes, A.I.J., de LP Duarte-Figueiredo, F., Assun\u00e7\u00e3o, R., Salles, J., and Loureiro, A.A. (2013, January 11). From Data to Knowledge: City-Wide Traffic Flows Analysis and Prediction Using Bing Maps. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Chicago, IL, USA.","DOI":"10.1145\/2505821.2505831"},{"key":"ref_66","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Advances in Neural Information Processing Systems, Curran Associates."},{"key":"ref_67","first-page":"26","article-title":"Lecture 6.5-Rmsprop: Divide the Gradient by a Running Average of Its Recent Magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"COURSERA Neural Netw. Mach. Learn."},{"key":"ref_68","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","unstructured":"(2018, June 22). Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_70","unstructured":"(2018, May 09). Internet Archive Wayback Machine. Available online: https:\/\/web.archive.org."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:53Z","timestamp":1760187113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/10\/2229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,14]]},"references-count":70,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19102229"],"URL":"https:\/\/doi.org\/10.3390\/s19102229","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,14]]}}}