{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:02:15Z","timestamp":1775066535059,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education","award":["2020R1I1A3052258"],"award-info":[{"award-number":["2020R1I1A3052258"]}]},{"name":"Ministry of Education","award":["RSP2024R164"],"award-info":[{"award-number":["RSP2024R164"]}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["2020R1I1A3052258"],"award-info":[{"award-number":["2020R1I1A3052258"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP2024R164"],"award-info":[{"award-number":["RSP2024R164"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing.<\/jats:p>","DOI":"10.3390\/s24123987","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T11:47:17Z","timestamp":1718797637000},"page":"3987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas"],"prefix":"10.3390","volume":"24","author":[{"given":"Bharathi Ramesh","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, Tamil Nadu, India"}]},{"given":"Narayanan","family":"Kumaran","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9229-5499","authenticated-orcid":false,"given":"Jayavelu Udaya","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6542-2050","authenticated-orcid":false,"given":"Sachin","family":"Salunkhe","sequence":"additional","affiliation":[{"name":"Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamil Nadu, India"},{"name":"Department of Mechanical Engineering, Faculty of Engineering, Gazi University, 06560 Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5547-7477","authenticated-orcid":false,"given":"Raja","family":"Venkatesan","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Republic of Korea"}]},{"given":"Ragavanantham","family":"Shanmugam","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Fairmont State University, Fairmont, WV 26554, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-7747","authenticated-orcid":false,"given":"Emad S.","family":"Abouel Nasr","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.matcom.2017.12.003","article-title":"A model and genetic algorithm for area-wide intersection signal optimization under user equilibrium traffic","volume":"155","author":"Guo","year":"2019","journal-title":"Math. Comput. Simul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.engappai.2017.07.022","article-title":"A group-based traffic signal control with adaptive learning ability","volume":"65","author":"Jin","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.engappai.2017.04.021","article-title":"A memetical gorithm for real world multi-intersection traffic signal optimization problems","volume":"63","author":"Sabar","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.trb.2021.06.010","article-title":"On the well-posedness of deterministic queuing networks with feedback control","volume":"150","author":"Como","year":"2021","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1177\/0361198119838842","article-title":"Real-Time Dynamic Traffic Control Based on Traffic-State Estimation","volume":"2673","author":"Ahmed","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Uppaluru, H., Liu, X., Emadi, H., and Rastgoftar, H. (2022, January 12\u201315). A Continuous-Time Optimal Control Approach to Congestion Control. Proceedings of the European Control Conference (ECC), London, UK.","DOI":"10.23919\/ECC55457.2022.9838036"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1016\/j.eng.2020.10.009","article-title":"Connected Vehicle-Based Traffic Signal Coordination","volume":"6","author":"Li","year":"2020","journal-title":"Engineering"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19919","DOI":"10.1038\/s41598-022-24469-y","article-title":"Research on highway traffic flow prediction model and decision-making method","volume":"12","author":"Zhu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhang, D.J., Zhang, X., Lai, K.K., and Su, B. (2021). Research on the Traffic Flow Control of Urban Occasional Congestion Based on Catastrophe Theory. J. Adv. Transp., 1341729.","DOI":"10.1155\/2021\/1341729"},{"key":"ref_10","first-page":"704","article-title":"Research on coordinated control of vehicle\u2019s speed in new mixed traffic flow","volume":"26","author":"Qu","year":"2021","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9396","DOI":"10.1038\/s41598-023-36606-2","article-title":"A traffic light control method based on multi-agent deep reinforcement learning algorithm","volume":"13","author":"Liu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Solaiappan, S., Kumar, B.R., Anbazhagan, N., Song, Y., Joshi, G.P., and Cho, W. (2023). Vehicular Traffic Flow Analysis and Minimize the Vehicle Queue Waiting Time Using Signal Distribution Control Algorithm. Sensors, 23.","DOI":"10.3390\/s23156819"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"136018","DOI":"10.1109\/ACCESS.2021.3116503","article-title":"Queueing Theory Based Vehicular Traffic Management System Through Jackson Network Model and Optimization","volume":"9","author":"Alam","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1109\/TITS.2017.2762085","article-title":"Optimal type-2 fuzzy system for arterial traffic signal control","volume":"19","author":"Bi","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1080\/19427867.2021.1953325","article-title":"Analytical model of road bottleneck queueing system","volume":"14","author":"Blazek","year":"2021","journal-title":"Transp. Lett."},{"key":"ref_16","first-page":"13926","article-title":"Queueing Model for Optimizing Vehicular Traffic Flow at a Signalized Intersection in a Developing Urban Center","volume":"29","author":"Oke","year":"2020","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.physa.2019.04.127","article-title":"Travel times, rational queueing and the macroscopic fundamental diagram of traffic flow","volume":"524","author":"Fiems","year":"2019","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_18","unstructured":"Motie, M., and Savla, K. (2017, January 9\u201314). On a vacation queue approach to queue size computation for a signalized traffic intersection. Proceedings of the 20th IFAC World Congress, Toulouse, France."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kim, D., Kim, S.-H., and Lee, H. (2017, January 3\u20136). Designing highway access control system using multi-classm\/g\/c\/c state dependent queueing model and cross-entropy method. Proceedings of the 2017 Winter Simulation Conference (WSC), Las Vegas, NV, USA.","DOI":"10.1109\/WSC.2017.8247887"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1109\/TVT.2015.2506629","article-title":"Dynamic Traffic Signal Timing Optimization Strategy Incorporating Various Vehicle Fuel Consumption Characteristics","volume":"65","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1049\/itr2.12300","article-title":"Human-centric multimodal deep (HMD) traffic signal control","volume":"17","author":"Wang","year":"2023","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116830","DOI":"10.1016\/j.eswa.2022.116830","article-title":"Reinforcement learning in urban network traffic signal control: A systematic literature review","volume":"199","author":"Noaeen","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/TVT.2021.3069921","article-title":"A novel reinforcement learning-based cooperative traffic signal system through max-pressure control","volume":"71","author":"Boukerche","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TITS.2020.3008612","article-title":"Deep reinforcement learning for intelligent transportation systems: A survey","volume":"23","author":"Haydari","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1109\/TVT.2019.2962514","article-title":"Reinforcement learning for joint control of traffic signals in a transportation network","volume":"69","author":"Lee","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rovetto, V., Cruz, E., Nu\u00f1ez, I., Santana, K., Smolarz, A., Rangel, J., and Cano, E.E. (2023). Minimizing Intersection Waiting Time: Proposal of a Queue Network Model Using Kendall\u2019s Notation in Panama City. Appl. Sci., 13.","DOI":"10.3390\/app131810030"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fedorova, E., Lapatin, I., Lizyura, O., Moiseev, A., Nazarov, A., and Paul, S. (2023). Queueing System with Two Phases of Service and Service Rate Degradation. Axioms, 12.","DOI":"10.3390\/axioms12020104"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, X., Ji, H., Hou, Z., and Fan, L. (2019). Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing. Energies, 12.","DOI":"10.3390\/en12071402"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3241207","DOI":"10.1155\/2023\/3241207","article-title":"Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections","volume":"2023","author":"Luo","year":"2023","journal-title":"J. Adv. Transport"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3141\/2623-06","article-title":"Real-Time Queue Length Estimation for Signalized Intersections Using Vehicle Trajectory Data","volume":"2623","author":"Li","year":"2017","journal-title":"J. Transp. Res. Board"},{"key":"ref_31","first-page":"012110","article-title":"Delay Time Analysis and Modelling of Signalised Intersections Using Global Positioning System (GPS) Receivers","volume":"671","author":"Alkaissi","year":"2020","journal-title":"Mater. Sci. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3987\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:01:20Z","timestamp":1760108480000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123987"],"URL":"https:\/\/doi.org\/10.3390\/s24123987","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]}}}