Smart City Gnosys

Smart city article details

Title	Implementing Ml And Iot-Based Predictive Traffic-Management Systems For Smart Cities
ID_Doc	30638
Authors	Sabeer S.; Ali S.S.; Siddiqua A.; Anjum A.
Year	2024
Published	2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024
DOI	http://dx.doi.org/10.1109/IC3TES62412.2024.10877447
Abstract	The exponential increase in the number of automobiles on the road has caused pollution, gridlock, and logistical transit delays in densely populated places. A new invention, the Internet of Things (IoT) is guiding the cosmos towards intelligent management systems and automated procedures. The advancement of automation and smart societies relies on this management of congestion and traffic control that are both effective and dependable and can save a lot of valuable resources. Autonomous cars and smart gadgets use an Internet of Things (IoT)-based ITM system of sensors to detect, collect, and communicate data. The transportation system may also benefit from machine learning (ML). Many problems plague current transport management methods, leading to gridlock, delays, and a high number of casualties. This study details the process of developing and launching an ATM that makes use of machine learning and the Internet of Things. Transportation, physical structures, and occurrences are the three pillars upon which the suggested system rests. The design takes into account every potential difficulty with the transportation system by using many scenarios. Using the machine learning-based DBSCAN clustering technology, the suggested ATM systems further guarantee that no inadvertent abnormalities are overlooked. The proposed ATM model can adapt the timing of the traffic lights based on current conditions as well as predicted movements at nearby crossings. Reducing traffic congestion eases vehicles over green lights and shortens travel times by improving the transition between turns. The experimental findings show that the proposed ATM system significantly outperformed the conventional traffic-management method, making it the ideal candidate for use in smart-city-based transportation planning. The proposed ATM system aims to reduce road accidents, improve the overall route experience, and minimise vehicle waiting durations. © 2024 IEEE.
Author Keywords	Intelligent Management system; Internet of things; Machine learning; Smart Gadgets; Traffic control

Similar Articles

Id	Similarity	Authors	Title	Published
18553	0.978	Lilhore U.K.; Imoize A.L.; Li C.-T.; Simaiya S.; Pani S.K.; Goyal N.; Kumar A.; Lee C.-C.	Design And Implementation Of An Ml And Iot Based Adaptive Traffic-Management System For Smart Cities	Sensors, 22, 8 (2022)
8520	0.954	Shamitha C.; Radhika S.; Malathy K.; Ranjith S.; Sasirekha N.	An Intelligent Iot Enabled Traffic Queue Handling System Using Machine Learning Algorithm	Proceedings of the 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022 (2022)
35933	0.951	Balasubramanian S.B.; Balaji P.; Munshi A.; Almukadi W.; Prabhu T.N.; Venkatachalam K.; Abouhawwash M.	Machine Learning Based Iot System For Secure Traffic Management And Accident Detection In Smart Cities	PeerJ Computer Science, 9 (2023)
19433	0.937	Al-Jawahry H.M.	Developing An Intelligent Traffic Management System For Smart Cities Through The Integration Of Machine Learning And Iot Technologies	Lecture Notes in Networks and Systems, 1306 LNNS (2025)
58554	0.923	Ouhmidou H.; Nabou A.; Ikidid A.; Bouassaba W.; Ouzzif M.; El Kiram M.A.	Traffic Control, Congestion Management And Smart Parking Through Vanet, Ml, And Iot: A Review	Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023 (2023)
6910	0.916	Dankan Gowda V.; Yogi K.S.; Srinivas I.V.; Kumar B.K.; Srinivas D.; Sudhakar Reddy N.	Ai And Machine Learning For Intelligent Traffic Management In Iot-Connected Cities	2024 Asian Conference on Intelligent Technologies, ACOIT 2024 (2024)
5762	0.913	Prakash J.; Murali L.; Manikandan N.; Nagaprasad N.; Ramaswamy K.	A Vehicular Network Based Intelligent Transport System For Smart Cities Using Machine Learning Algorithms	Scientific Reports, 14, 1 (2024)
42891	0.913	Mirza N.M.; Ali A.; Shifa N.; Ishak M.K.; Ammar K.	Predictive Modeling For Smart Traffic Systems: Harnessing Iot Data Insights	2023 24th International Arab Conference on Information Technology, ACIT 2023 (2023)
22878	0.91	Anu Priya S.; Rajesh kanna B.; Beaulah Jeyavathana R.; Bhat N.; Rajalakshmi S.; Srimathi S.	Employing A Deep Learning Technique To Categorize Internet Of Things (Iot) Traffic In A Smart City Context	2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023 (2023)
22879	0.909	Mahesh C.; Sumithra M.; Rao Ranga M.; Kumar K.R.; Suganthi D.; Karthiyayini S.	Employing A Deep Learning Technique To Categorize Internet Of Things (Iot) Traffic In A Smart City Context	Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023 (2023)