{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T05:11:59Z","timestamp":1777698719654,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.<\/jats:p>","DOI":"10.3390\/s20216218","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T21:39:56Z","timestamp":1604180396000},"page":"6218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights"],"prefix":"10.3390","volume":"20","author":[{"given":"Rodrigo","family":"Carvalho Barbosa","sequence":"first","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Shoaib Ayub","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5839-0692","authenticated-orcid":false,"given":"Renata","family":"Lopes Rosa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5401-7551","authenticated-orcid":false,"given":"Dem\u00f3stenes","family":"Zegarra Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37200-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lunchakorn","family":"Wuttisittikulkij","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4728","DOI":"10.1109\/TITS.2019.2945793","article-title":"RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User\u2019s QoE","volume":"20","author":"Lasmar","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/COMST.2014.2339817","article-title":"A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches","volume":"17","author":"Djahel","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","first-page":"10","article-title":"Urban traffic congestion: The problem and solutions","volume":"2","author":"Kumarage","year":"2004","journal-title":"Asian Econ. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/TITS.2016.2600300","article-title":"Vehicles of the Future: A Survey of Research on Safety Issues","volume":"18","author":"Bila","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yadav, R.K., Jain, R., Yadav, S., and Bansal, S. (2020, January 13\u201315). Dynamic Traffic Management System Using Neural Network based IoT System. Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121066"},{"key":"ref_6","unstructured":"Manville, C., Cochrane, G., Cave, J., Millard, J., Pederson, J., Thaarup, R., Liebe, A., Wissner, M., Massink, R., and Kotterink, B. (2014). Mapping Smart Cities in the EU. Study Directorate-General for Internal Policies, European Union."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/rob.20147","article-title":"Stanley: The robot that won the DARPA Grand Challenge","volume":"23","author":"Thrun","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.trpro.2020.02.049","article-title":"How Do Autonomous Cars Work?","volume":"44","author":"Kolla","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Behrendt, K., Novak, L., and Botros, R. (June, January 29). A deep learning approach to traffic lights: Detection, tracking, and classification. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989163"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pat. Anal. and Mach. Intel."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 17\u201319). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). SSD: Single Shot MultiBox Detector. Computer Vision, Springer International Publishing."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Tsai, C. (2016, January 26\u201330). Pedestrian, bike, motorcycle, and vehicle classification via deep learning: Deep belief network and small training set. Proceedings of the 2016 International Conference on Applied System Innovation (ICASI), Okinawa, Japan.","DOI":"10.1109\/ICASI.2016.7539822"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, J.T., and Chung, Y. (2017, January 21\u201326). Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.127"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.comcom.2020.02.069","article-title":"Applications of Artificial Intelligence and Machine learning in smart cities","volume":"154","author":"Ullah","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.3390\/s16111892","article-title":"Traffic management for emergency vehicle priority based on visual sensing","volume":"16","author":"Nellore","year":"2016","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, L., and Chen, W. (2017, January 16\u201319). Intelligent traffic light control using distributed multi-agent Q learning. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317730"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"M\u00fcller, J., and Dietmayer, K. (2018). Detecting traffic lights by single shot detection. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE.","DOI":"10.1109\/ITSC.2018.8569683"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gu, S., Ding, L., Yang, Y., and Chen, X. (2017, January 11\u201312). A new deep learning method based on AlexNet model and SSD model for tennis ball recognition. Proceedings of the 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), Hiroshima, Japan.","DOI":"10.1109\/IWCIA.2017.8203578"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Zhu, X. (2019, January 19\u201321). Vehicle Detection in the Aerial Infrared Images via an Improved Yolov3 Network. Proceedings of the 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China.","DOI":"10.1109\/SIPROCESS.2019.8868430"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Li, D., Huo, S., and Kung, S.Y. (2020). Soft-Root-Sign Activation Function. arXiv.","DOI":"10.1016\/j.eswa.2020.114534"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Malik, F., Khattak, H.A., and Ali Shah, M. (2019, January 5\u20137). Evaluation of the Impact of Traffic Congestion Based on SUMO. Proceedings of the 2019 25th International Conference on Automation and Computing (ICAC), Lancaster, UK.","DOI":"10.23919\/IConAC.2019.8895120"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vachan, B.R., and Mishra, S. (2019, January 12\u201315). A User Monitoring Road Traffic Information Collection Using SUMO and Scheme for Road Surveillance with Deep Mind Analytics and Human Behavior Tracking. Proceedings of the 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China.","DOI":"10.1109\/ICCCBDA.2019.8725761"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cruz-Piris, L., Rivera, D., Fernandez, S., and Marsa-Maestre, I. (2018). Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management. Sensors, 18.","DOI":"10.3390\/s18020435"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zambrano-Martinez, J., Calafate, C., Soler, D., Cano, J.C., and Manzoni, P. (2018). Modeling and Characterization of Traffic Flows in Urban Environments. Sensors, 18.","DOI":"10.3390\/s18072020"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zambrano-Martinez, J., Calafate, C., Soler, D., and Cano, J.C. (2017). Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions. Sensors, 17.","DOI":"10.3390\/s17122921"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51621","DOI":"10.1109\/ACCESS.2020.2980626","article-title":"Coordinated Control Algorithm at Non-Recurrent Freeway Bottlenecks for Intelligent and Connected Vehicles","volume":"8","author":"Xiaoping","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/LSP.2017.2773536","article-title":"Speech Quality Assessment Over Lossy Transmission Channels Using Deep Belief Networks","volume":"25","author":"Affonso","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, D.Z., and M\u00f6ller, S. (2019, January 5\u20137). Speech Quality Parametric Model that Considers Wireless Network Characteristics. Proceedings of the 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany.","DOI":"10.1109\/QoMEX.2019.8743346"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1109\/TII.2018.2867174","article-title":"A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning","volume":"15","author":"Rosa","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, R., Rodr\u00edguez, D.Z., Rosa, R.L., and Bressan, G. (2016, January 28\u201330). Recommendation system using sentiment analysis considering the polarity of the adverb. Proceedings of the 2016 IEEE International Symposium on Consumer Electronics (ISCE), Sao Paulo, Brazil.","DOI":"10.1109\/ISCE.2016.7797377"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"77022","DOI":"10.1109\/ACCESS.2018.2871072","article-title":"Speech Quality Assessment in Wireless VoIP Communication Using Deep Belief Network","volume":"6","author":"Affonso","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10805","DOI":"10.1109\/ACCESS.2017.2706674","article-title":"Age Groups Classification in Social Network Using Deep Learning","volume":"5","author":"Rosa","year":"2017","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object Detection With Deep Learning: A Review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","first-page":"1440","article-title":"Fast R-CNN","volume":"5","author":"Girshick","year":"2015","journal-title":"Microsoft Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fan, Q., Brown, L., and Smith, J. (2016, January 19\u201322). A closer look at Faster R-CNN for vehicle detection. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535375"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., and Kumar, M. (2020, January 6\u20137). YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"ref_41","unstructured":"Ning, C., Zhou, H., Song, Y., and Tang, J. (2017, January 10\u201314). Inception Single Shot MultiBox Detector for object detection. Proceedings of the 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Hong Kong, China."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jeong, J., Park, H., and Kwak, N. (2017). Enhancement of SSD by concatenating feature maps for object detection. arXiv.","DOI":"10.5244\/C.31.76"},{"key":"ref_43","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A Survey of Deep Learning-Based Object Detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_46","unstructured":"Redmon, J., and Farhadi, A. (2019). Yolov3: An incremental improvement. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Du, L., Chen, W., Fu, S., Kong, H., Li, C., and Pei, Z. (2019, January 14\u201317). Real-time Detection of Vehicle and Traffic Light for Intelligent and Connected Vehicles Based on YOLOv3 Network. Proceedings of the 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK.","DOI":"10.1109\/ICTIS.2019.8883761"},{"key":"ref_48","first-page":"562","article-title":"Pedestrian Detection for Transformer Substation Based on Gaussian Mixture Model and YOLO","volume":"Volume 2","author":"Peng","year":"2016","journal-title":"2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ren, P., Fang, W., and Djahel, S. (2017). A novel YOLO-Based real-time people counting approach. International Smart Cities Conference (ISC2), IEEE.","DOI":"10.1109\/ISC2.2017.8090864"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lan, W., Dang, J., Wang, Y., and Wang, S. (2018, January 5\u20138). Pedestrian Detection Based on YOLO Network Model. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, China.","DOI":"10.1109\/ICMA.2018.8484698"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Chen, B. (2019, January 24\u201325). Real-Time Pedestrian Detection Based on Improved YOLO Model. Proceedings of the International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2019.10101"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dasgupta, M., Bandyopadhyay, O., and Chatterji, S. (2019, January 6\u20138). Automated Helmet Detection for Multiple Motorcycle Riders using CNN. Proceedings of the IEEE Conference on Information and Communication Technology, Allahabad, India.","DOI":"10.1109\/CICT48419.2019.9066191"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lam, C.T., Ng, B., and Chan, C.W. (2019, January 16\u201319). Real-Time Traffic Status Detection from on-Line Images Using Generic Object Detection System with Deep Learning. Proceedings of the International Conference on Communication Technology (ICCT), Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947064"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lou, L., Zhang, Q., Liu, C., Sheng, M., Zheng, Y., and Liu, X. (2019, January 24\u201327). Vehicles Detection of Traffic Flow Video Using Deep Learning. Proceedings of the Data Driven Control and Learning Systems Conference (DDCLS), Dali, China.","DOI":"10.1109\/DDCLS.2019.8908873"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, S., and Lin, W. (2019, January 11\u201313). Embedded System Real-Time Vehicle Detection based on Improved YOLO Network. Proceedings of the 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China.","DOI":"10.1109\/IMCEC46724.2019.8984055"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama, S. (2017). Speed\/Accuracy Trade-Offs for Modern Convolutional Object Detectors. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Gong, H., Li, H., Xu, K., and Zhang, Y. (2019, January 22\u201324). Object Detection Based on Improved YOLOv3-tiny. Proceedings of the Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8996750"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, L., Ye, F., Ruan, Y., Fan, H., and Chen, Q. (2018). An algorithm for highway vehicle detection based on convolutional neural network. Eurasip J. Image Video Process., 2018.","DOI":"10.1186\/s13640-018-0350-2"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chun, D., Choi, J., Kim, H., and Lee, H. (2019, January 23\u201326). A Study for Selecting the Best One-Stage Detector for Autonomous Driving. Proceedings of the International Technical Conference on Circuits\/Systems, Computers and Communications (ITC-CSCC), JeJu, Korea.","DOI":"10.1109\/ITC-CSCC.2019.8793291"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., and Ouni, K. (2019, January 5\u20137). Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3. Proceedings of the 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.","DOI":"10.1109\/UVS.2019.8658300"},{"key":"ref_61","unstructured":"Choi, J., Chun, D., Kim, H., and Lee, H.J. (November, January 27). Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, Z., Xu, W., Wang, Z., He, X., Yang, F., and Yin, Z. (2019, January 16\u201319). Combining Yolov3-tiny Model with Dropblock for Tiny-face Detection. Proceedings of the International Conference on Communication Technology (ICCT), Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947158"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Moghaddam, M.J., Hosseini, M., and Safabakhsh, R. (2015). Traffic light control based on fuzzy Q-leaming. 2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP), IEEE.","DOI":"10.1109\/AISP.2015.7123500"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zaid, A.A., Suhweil, Y., and Yaman, M.A. (2017, January 11\u201313). Smart controlling for traffic light time. Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, Jordan.","DOI":"10.1109\/AEECT.2017.8257768"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1742-6596\/1188\/1\/012001","article-title":"Modeling and simulation of queue waiting time at traffic light intersection","volume":"1188","author":"Harahap","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kanungo, A., Sharma, A., and Singla, C. (2014, January 6\u20138). Smart traffic lights switching and traffic density calculation using video processing. Proceedings of the Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, India.","DOI":"10.1109\/RAECS.2014.6799542"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Tahmid, T., and Hossain, E. (2017, January 7\u20139). Density based smart traffic control system using canny edge detection algorithm for congregating traffic information. Proceedings of the International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh.","DOI":"10.1109\/EICT.2017.8275131"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11036-015-0571-x","article-title":"Smart traffic light for low traffic conditions","volume":"20","author":"Silva","year":"2015","journal-title":"Mob. Netw. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"D\u00edaz, N., Guerra, J., and Nicola, J. (2018). Smart Traffic Light Control System. Third Ecuador Technical Chapters Meeting (ETCM), IEEE.","DOI":"10.1109\/ETCM.2018.8580282"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Suthaputchakun, C., and Pagel, A. (2019, January 18\u201320). A Novel Priority-Based Ambulance-to-Traffic Light Communication for Delay Reduction in Emergency Rescue Operations. Proceedings of the International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Paris, France.","DOI":"10.1109\/ICT-DM47966.2019.9032930"},{"key":"ref_71","unstructured":"Ministry of Cities (2008). National Traffic Council and National Traffic Department. Brazilian Traffic Code and Complementary Legislation in Force, CONTRAN."},{"key":"ref_72","unstructured":"Lima Bastos, Y.G., and de Andrade, S.M. (1999). Traffic Accidents and the New Brazilian Traffic Code in the Cities of the Southern Region of Brazil."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ash, R., Ofri, D., Brokman, J., Friedman, I., and Moshe, Y. (2018, January 12\u201314). Real-time Pedestrian Traffic Light Detection. Proceedings of the IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), Eilat, Israel.","DOI":"10.1109\/ICSEE.2018.8646287"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Varma, B., Sam, S., and Shine, L. (2019, January 6\u20138). Vision Based Advanced Driver Assistance System Using Deep Learning. Proceedings of the International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.","DOI":"10.1109\/ICCCNT45670.2019.8944842"},{"key":"ref_76","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_77","unstructured":"Chen, H., Wang, Y., Shi, Y., Yan, K., Geng, M., Tian, Y., and Xiang, T. (2016). Deep Transfer Learning for Person Re-Identification. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Huang, Y.Q., Zheng, J.C., Sun, S.D., Chen, C.Y., and Liu, J. (2020). Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections. Appl. Sci., 10.","DOI":"10.3390\/app10093079"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3141\/1802-28","article-title":"Comparison of Greenshields, Pipes, and Van Aerde Car-Following and Traffic Stream Models","volume":"1802","author":"Rakha","year":"2002","journal-title":"Transp. Res. Rec."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:25Z","timestamp":1760178445000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,31]]},"references-count":79,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216218"],"URL":"https:\/\/doi.org\/10.3390\/s20216218","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,31]]}}}