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Journal articles on the topic 'Intelligent traffic management'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

V, Kishen, M. S. Sathvik Murthy, Mithilesh Kumar, and Nimrita Koul. "Intelligent Traffic Management System." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 130–32. http://dx.doi.org/10.35940/ijeat.e1027.0585s19.

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The importance of traffic signals is increasing owing to the drastic increase in population. Ensuring road safety is of high priority. In this project, we introduce an Intelligent Traffic Management System (ITMS) capable of managing traffic of varying densities, without the need of a traffic warden to physically monitor a particular intersection. This system is designed to retrieve the live traffic feed from a junction and process the same using the TensorFlow Object Detection API over OpenCV to detect the severity of the traffic based on the number of vehicles detected. Upon determining the n

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Korjagin, Sergey, Ksenia Polupan, Pavel Klachek, Alexey Pyatikop, and Evgeniy Koryagin. "Intelligent road traffic management based on the system of fuzzy situational management and virtual cyberspace." MATEC Web of Conferences 334 (2021): 01013. http://dx.doi.org/10.1051/matecconf/202133401013.

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Theoretical and applied ideas and tools of traffic intelligent management on the basis of fuzzy situational management and virtual cyberspace are developed through the system approach, artificial intelligence methods, modern achievements in the field of the creation of intelligent transport systems (ITS). The proposed scientifically-methodical foundations and software and hardware tools allow the creation of intelligent transport systems at a new level, synchronizing the development of road traffic infrastructure and virtual cyberspace, allowing to solve effectively a rather large range of the

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R, Jyothi Priya, Prameela Paramasivam, Thava Bharathi Kanagaraj, and Sivasankari Paramasivan. "A Benchmark Example of Intelligent Traffic Management System using Artificial Intelligence." INCOSE International Symposium 33, S1 (2023): 76–89. http://dx.doi.org/10.1002/iis2.13116.

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AbstractThis paper describes a practical methodology for benchmarking the Intelligent Traffic Management System (ITMS) of Smart City Application. It explains the complete development of the application from defining requirements to verifying the software. The system design is demonstrated using MATLAB System Composer. To achieve the intelligent traffic system, we used the You Only Look Once (YOLO) v5 algorithm for vehicle detection. The other deep learning prediction logics are shown to realize the various features such as the optimal route detection, hi‐traffic detection of ITMS. Followed by

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Wu, Rui. "Development Status of Intelligent Traffic Management System." Academic Journal of Science and Technology 10, no. 2 (2024): 153–55. http://dx.doi.org/10.54097/cgbmnp04.

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The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such a

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Zhang, Xuanning. "Artificial Intelligence in Intelligent Traffic Signal Control." Applied and Computational Engineering 118, no. 1 (2025): 113–20. https://doi.org/10.54254/2755-2721/2025.20846.

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With the rapid urbanization and the increasing traffic demand in cities, traffic congestion and accidents have become significant challenges for urban transportation systems. Traditional traffic signal control systems, which rely on fixed signal cycles, often fail to adapt to real-time traffic conditions, leading to inefficiencies and resource waste. This paper explores the application of Artificial Intelligence (AI) in intelligent traffic signal control systems. Specifically, it focuses on the use of Deep Reinforcement Learning (DRL), particularly the Deep Q-Network (DQN) model, for optimizin

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Thompson, John A. "AI-Integrated IoT Networks for Smart City Traffic Management." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–10. https://doi.org/10.63665/ijicsitr.v1i02.01.

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The increase in rate of urban population and increasing urban infrastructure complexity have created humongous issues of traffic management. Conventional ways of urban traffic management are not efficient, creating traffic congestion, time delay, and adverse environmental effects. Smart traffic management of a smart city through integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is a paradigm-shift solution to all such issues. This article discusses how AI and IoT networks are converging to design adaptive, dynamic, and intelligent traffic systems. IoT sensors, camera

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Bonthu, Bhulakshmi, A. Vishal Narayanan, and J. Jabanjalin Hilda. "Intelligent Traffic Management by Synchronized Signalling." Asian Journal of Computer Science and Technology 5, no. 1 (2016): 17–20. http://dx.doi.org/10.51983/ajcst-2016.5.1.1763.

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Traffic congestion is one of the major problems faced in our day-to-day life. The objective of this paper is to provide an innovative method to solve traffic congestion. In the present day scenario, there are numerous Traffic signals which delay the time taken to reach a destination. In order to overcome this problem, we need to synchronize the signals. The goal of this project is to develop a system which synchronizes the signals so that congestion is managed in better manner. Here, signals across neighboring junctions are synchronized in cooperative method and congestion will be cleared in a

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M. Vijai Kishore, M. Vijai Kishore, Achal Garg, and Onika Arora. "Intelligent Transportation Systems for Traffic Management in Dehradun City." International Journal of Scientific Research 3, no. 4 (2012): 146–47. http://dx.doi.org/10.15373/22778179/apr2014/50.

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H, Patel Hanil, Vasava Vandana M, Parmarth Rajveer D, et al. "Intelligent Transportation System: Enhancing Traffic Management and Road Safety." International Journal of Research Publication and Reviews 4, no. 9 (2023): 1185–93. http://dx.doi.org/10.55248/gengpi.4.923.52847.

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Wu, Yun Na, and Ru Hang Xu. "Intelligent Research for Chinese Intelligent Urban Construction Problems and Solutions - Intelligent Traffic Forecast." Advanced Materials Research 951 (May 2014): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amr.951.3.

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Smart city is a promising form for future city management. Intelligent transportation plays an important role in the smart city. Traffic forecast is an important way to realize intelligent traffic. This paper proposed a method to solve the complex mapping problem in traffic forecast based on BP artificial neural intelligence method. Data of the City of Alexandria in the U.S. is used to testify the feasibility of the method. The result shows that this method shows good performance in solving complex mapping problem.

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Ismail, Mahmoud, and Shereen Zaki. "Intelligent Traffic Management System for Smart Cities." Journal of Intelligent Systems and Internet of Things 3, no. 1 (2021): 43–50. http://dx.doi.org/10.54216/jisiot.030104.

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rapid urbanization and the growing population in smart cities pose significant challenges to the management of urban traffic. In recent years, there has been an increasing interest in developing intelligent traffic management systems that leverage advanced machineries, such as the Internet of Things (IoT), and machine learning (ML), to enhance the efficiency and effectiveness of traffic management in smart cities. This paper proposes an intelligent traffic management (ITM) system for smart cities that integrates various computing paradigms to provide real-time traffic information, optimize tra

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Reji, Noyal, Philips Jose, Sanjayramprasad V S, Aju Joseph, and Alpha Mathew. "Smart Traffic Systems: A Comprehensive Overview." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2024): 1–7. https://doi.org/10.55041/ijsrem41556.

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Abstract—Urban traffic congestion represents a critical chal-lenge for modern cities, significantly impacting mobility, envi-ronmental sustainability, and quality of urban life. This com-prehensive survey explores the transformative potential of ad-vanced technologies—particularly deep reinforcement learning (DRL), artificial intelligence (AI), and Vehicle-to- Everything (V2X) communication—in revolutionizing traffic management systems. We critically examine emerging approaches that move beyond traditional fixed-timing signals, highlighting innovative methodologies including deep reinforcement

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Fang, Dan Yu. "Research of Intelligent Traffic Management System." Applied Mechanics and Materials 340 (July 2013): 662–64. http://dx.doi.org/10.4028/www.scientific.net/amm.340.662.

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With the growing economic level and the continuous development of information network technology, as well as the continuous improvement of people's living standards, transportation demand gradually increased. Due to the current transportation system in China has been facing the problem of traffic congestion and inefficient, and cause serious pollution and energy shortages, and intelligent traffic management system is the key to solve these problems, the modern information network technology and transportation combined. This article gives a brief introduction to the development and study of the

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Damadam, Shima, Mojtaba Zourbakhsh, Reza Javidan, and Azadeh Faroughi. "An Intelligent IoT Based Traffic Light Management System: Deep Reinforcement Learning." Smart Cities 5, no. 4 (2022): 1293–311. http://dx.doi.org/10.3390/smartcities5040066.

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Traffic is one of the indispensable problems of modern societies, which leads to undesirable consequences such as time wasting and greater possibility of accidents. Adaptive Traffic Signal Control (ATSC), as a key part of Intelligent Transportation Systems (ITS), plays a key role in reducing traffic congestion by real-time adaptation to dynamic traffic conditions. Moreover, these systems are integrated with Internet of Things (IoT) devices. IoT can lead to easy implementation of traffic management systems. Recently, the combination of Artificial Intelligence (AI) and the IoT has attracted the

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Guo, Aipeng, and Chunhui Yuan. "Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence." Electronics 10, no. 6 (2021): 700. http://dx.doi.org/10.3390/electronics10060700.

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For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelli

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P., Sonali, Priyanka H., Gayatri D., and Kalyani G. "Intelligent Traffic Management based on IoT." International Journal of Computer Applications 157, no. 2 (2017): 26–28. http://dx.doi.org/10.5120/ijca2017912639.

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Premji, Abhijit, Amarjjit Premji, Premjith P., and Preetha P.S. "Intelligent Traffic Management System for India." Ecology, Environment and Conservation 30, Suppl (2024): S344—S348. http://dx.doi.org/10.53550/eec.2024.v30i04s.059.

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Goel, Praveen, R. H. Goudar, Rohit Malik, Ranjeet Singh, and Nitish Kumar Singh. "Localization based intelligent traffic management system." International Journal of System Assurance Engineering and Management 8, S1 (2015): 90–98. http://dx.doi.org/10.1007/s13198-015-0407-x.

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A. Sakr, Hesham, Magda I. El-Afifi, and plvar team. "Intelligent Traffic Management Systems: A review." Nile Journal of Communication and Computer Science 5, no. 1 (2023): 42–56. http://dx.doi.org/10.21608/njccs.2023.321169.

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Mazurenko, Roman, and Bohdan Yeremenko. "Intelligent traffic management system of a big city: ontology concept “decision models”." Management of Development of Complex Systems, no. 57 (March 29, 2024): 174–80. http://dx.doi.org/10.32347/2412-9933.2024.57.174-180.

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The problem of traffic jams is still relevant for the transport system of many large cities on different continents. The work is aimed at the formation of an ontologically controlled technology, which is based on the use of available means of data production, information resources, modelling of traffic forecasting of large cities and traffic management based on these forecasts. That is why it pays great attention to the research of innovative solutions to the problem of optimizing traffic through the city network using big data, artificial intelligence models, and Internet of Things technologi

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Huang, Shuyue. "Mechatronics Application and Performance Analysis in Intelligent Transportation Systems." Applied and Computational Engineering 126, no. 1 (2025): 95–101. https://doi.org/10.54254/2755-2721/2025.20055.

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This paper presents a comprehensive analysis of the integration of mechatronics in intelligent transportation systems (ITS), elucidating its functional capabilities and limitations. As urbanization accelerates and traffic demand rises, the ITS has emerged as vital solutions for addressing traffic congestion and safety issues. In this regard, mechatronics technology plays a key role by enhancing the intelligence of traffic management, thereby improving the overall efficiency and safety of transportation networks. Thus, the paper provides a detailed analysis of the mechatronics application in in

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Jawad, Sara Sadiq, Dheyaa Jasim Kadhim, and Yusmadi Yah Bt Jusoh. "Insight Thoughts for Intelligent Traffic Management-Based SDN." Journal of Engineering 31, no. 7 (2025): 1–34. https://doi.org/10.31026/j.eng.2025.07.01.

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The trend towards studying traffic management in software-defined networks (SDN) is increasing widely due to its great importance in enhancing the efficiency of networks and their abilities to adapt to the increasing demands on data and modern applications. What increases the importance of these studies is the integration of artificial intelligence (AI) technologies, which in turn provide intelligent analysis and response capabilities that contribute to improving quality of service (QoS), avoiding congestion, and achieving balanced load distribution across the network. Intelligent management i

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Dong, Zishuo. "Intelligent traffic signals: Pivotal innovations for sustainable urban traffic management." Theoretical and Natural Science 14, no. 1 (2023): 165–72. http://dx.doi.org/10.54254/2753-8818/14/20240936.

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As living standards rise, there is a noticeable surge in the number of private vehicles. This increase places considerable strain on urban transportation, leading to significant congestion in metropolitan areas. This research delves into the establishment of intelligent traffic control, focusing specifically on the signal light control system to address this growing concern. The realm of intelligent transportation seeks to bolster the efficiency, safety, and environmental sustainability of transit systems. To discern variations and identify potential bottlenecks in traffic flow, the background

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T M, Inba Malar, Bharatha Sreeja G, Amala Justus Selvam M, et al. "Intelligent Traffic Control System Using Deep Learning." ECS Transactions 107, no. 1 (2022): 2783–90. http://dx.doi.org/10.1149/10701.2783ecst.

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Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, typ

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Lin, Qiang. "Research on Traffic Informatization and Intelligent Construction Based on Urban Traffic Big Data." Academic Journal of Science and Technology 4, no. 2 (2023): 125–28. http://dx.doi.org/10.54097/ajst.v4i2.4121.

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Urban traffic is a new traffic demand network defined by residents' travel behavior and community relationship. From the management level, we should actively study and explore the construction of urban traffic informatization and intelligence, strengthen the operation efficiency of infrastructure and the dynamic supervision of traffic in various fields and subsystems of the city, and promote the integrated management of urban network operation, management, emergency response, treatment and maintenance. Under the background of big data, build an information and intelligent system of urban traff

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LYAPIN, S. A., Y. N. RIZAEVA, and D. A. KADASEV. "PROACTIVE MANAGEMENT OF THE CITY'S TRAFFIC." World of transport and technological machines 73, no. 2 (2021): 81–91. http://dx.doi.org/10.33979/2073-7432-2021-73-2-81-91.

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The article shows that with the growing traffic on motorways, the demand for modern traffic management systems for effective traffic flow management is generated. Based on the analysis of the METANET model and algorithms for managing the entrance to the highway, an algorithm is proposed that uses the capabilities of an intelligent transport and logistics system to implement proactive traffic management. The possibility of using self-driving cars to supplement the information about traffic characteristics obtained from stationary sensors of the transport infrastructure of the intelligent transp

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Shunu, Daniel. "IMPROVING TRAFFIC FLOW AT INTERSECTION USING INTELLIGENT TRAFFIC MANAGEMENT SYSTEM." Computer Science & IT Research Journal 1, no. 2 (2020): 65–70. http://dx.doi.org/10.51594/csitrj.v1i2.137.

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In this study, a proposed intelligent traffic management system is presented making use of the wireless sensor network for improving traffic flow. By making use of the clustering algorithm, VANET environment is utilized for the proposed system. The components of the proposed system include sensor node hardware, vehicle detection system through magnetometer, and UDP protocol for communication between the nodes. The intersection control agent receives the information about the vehicles and by making use of its algorithm, it dynamically changes the traffic light timings. By making use of the gree

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Sun, Peixiang, Qifeng Yu, and Kesi You. "Intelligent traffic management strategy for traffic congestion in underground loop." Tunnelling and Underground Space Technology 143 (January 2024): 105509. http://dx.doi.org/10.1016/j.tust.2023.105509.

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Xu, Zheng, Jiaqiang Yuan, Liqiang Yu, Guanghui Wang, and Mingwei Zhu. "Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 18–27. http://dx.doi.org/10.62051/ijcsit.v2n1.03.

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With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex tempora

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Mazurenko, Roman, and Bohdan Yeremenko. "Intelligent road transport flow management system: Basic ontology concepts." Management of Development of Complex Systems, no. 55 (September 25, 2023): 192–97. http://dx.doi.org/10.32347/2412-9933.2023.55.192-197.

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The work is aimed at solving the problem of traffic jams that occur at complex intersections. The problem is considered using the example of the city of Kyiv. However, it remains relevant for residents of many large cities. In this article here we consider the issues of creating ontology. It will later become the basis for forming the knowledge bases of several intelligent traffic management systems intended for traffic management at typical intersections in large cities. The scheme of operation of the system, which is being developed for the situational control of a complex of traffic lights

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Li, Tengfei, Xuanrui Xiong, Guifeng Zheng, Ying Li, and Amr Tolba. "A Blockchain-Based Shared Bus Service Scheduling and Management System." Sustainability 15, no. 16 (2023): 12516. http://dx.doi.org/10.3390/su151612516.

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With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can redu

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Channappa A. "A study on intelligent traffic management using IoT for urban congestion control." World Journal of Advanced Research and Reviews 1, no. 3 (2019): 085–91. https://doi.org/10.30574/wjarr.2019.1.3.0117.

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Intelligent Traffic Management Systems (ITMS) play a vital role in enhancing the efficiency, safety, and sustainability of urban transportation networks. The research explores the integration of artificial intelligence, IoT, and data analytics in traffic management, highlighting their impact on congestion reduction, accident prevention, and environmental sustainability. Additionally, it discusses the future directions and potential avenues for further research in this domain.A smart city must have an intelligence-based traffic management system for the movement of vehicles. Traffic jams and ve

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Lu, Sheng. "Study on Traffic Overload Management for ICIE." Key Engineering Materials 474-476 (April 2011): 870–73. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.870.

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This paper has an analysis on actions of traffic throughput and traffic overload management based on a new intelligent controller for industrial Ethernet: ICIE (Intelligent Controller for Industrial Ethernet). It proposed a new mechanism to minimize the congestion which is based on the taking an adaptive decision during transferring multicast messages. It also focuses on the actions analysis on traffic throughput to achieve and improve the traffic throughput goal. It has a further discussion on four conditions of traffic management: detection of traffic overload, measures to be taken to avoid

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Zhang, Yining. "Research and Application of Intelligent Pedestrian Traffic Light System." Communications in Humanities Research 44, no. 1 (2024): 139–48. http://dx.doi.org/10.54254/2753-7064/44/20240076.

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This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classificat

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Zhang, Yining. "Research and Application of Intelligent Pedestrian Traffic Light System." Communications in Humanities Research 36, no. 1 (2024): 148–57. http://dx.doi.org/10.54254/2753-7064/36/20240076.

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This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classificat

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Nijaat Zaman, Nijaat Zaman. "AN INTELLIGENT TRAFFIC CONTROL SYSTEM FOR BAKU." PIRETC-Proceeding of The International Research Education & Training Centre 24, no. 03 (2023): 04–10. http://dx.doi.org/10.36962/piretc24032023-04.

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This article introduces a new traffic control system framework, Mobile Intelligent Traffic Management System (MITCS), designed for Baku for the next generation. According to statistics, every year people lose 154 hours in traffic on average. 1.3 million people die in accidents. The system combines micro-mechanical and electrical technologies embedded system, wireless transmission, image processing, and solar module. The objectives of this study are: 1. Research and design new and multifunctional traffic controller in the box; 2. Design a cost-effective basis contribution to the communication n

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Xu, Meng, Xiao Dong Pan, Long Xi Sun, Gang Yan, and Feng Chen. "Intelligent Traffic System Design Research of Urban Complex Underground Garage." Applied Mechanics and Materials 743 (March 2015): 715–23. http://dx.doi.org/10.4028/www.scientific.net/amm.743.715.

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Reasonable traffic system design and management has vital significance to ensure the safe and smooth traffic operation of urban complex and urban traffic system. Based on the characteristics of urban complex, this paper analyzed intelligent traffic system design of underground garage of urban complex. The paper proposed a reasonable traffic system design method to ensure the smoothness and safety of urban complex traffic. Meanwhile the full-video intelligent parking garage management system with advantages of safe and advanced technology was adopted to realize user-friendly, intelligent and au

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Shirsath, Vaishali, Vikas Kaul, R. Sampath Kumar, and Bhushankumar Nemade. "INTELLIGENT TRAFFIC MANAGEMENT FOR VEHICULAR NETWORKS USING MACHINE LEARNING." ICTACT Journal on Communication Technology 14, no. 3 (2023): 2998–3004. http://dx.doi.org/10.21917/ijct.2023.0446.

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As urbanization and vehicular density continue to rise, the efficient management of traffic in vehicular networks becomes increasingly critical. This paper presents an innovative approach to intelligent traffic management leveraging Machine Learning (ML) techniques, specifically employing Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The integration of SVM with RBF proves to be particularly effective in capturing complex non-linear relationships within the dynamic and unpredictable vehicular environment. Our proposed system aims to enhance traffic flow, reduce congest

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Aouedi, Ons, Kandaraj Piamrat, and Benoît Parrein. "Intelligent Traffic Management in Next-Generation Networks." Future Internet 14, no. 2 (2022): 44. http://dx.doi.org/10.3390/fi14020044.

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The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These met

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Madake, Satyraj Madake. "Intelligent Traffic Management System for Urban Conditions." International Journal of Scientific Research and Engineering Trends 10, no. 6 (2024): 2315–25. http://dx.doi.org/10.61137/ijsret.vol.10.issue6.332.

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Milanes, Vicente, Jorge Villagra, Jorge Godoy, Javier Simo, Joshué Perez, and Enrique Onieva. "An Intelligent V2I-Based Traffic Management System." IEEE Transactions on Intelligent Transportation Systems 13, no. 1 (2012): 49–58. http://dx.doi.org/10.1109/tits.2011.2178839.

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Hernández, Josefa Z., Sascha Ossowski, and Ana Garcı́a-Serrano. "Multiagent architectures for intelligent traffic management systems." Transportation Research Part C: Emerging Technologies 10, no. 5-6 (2002): 473–506. http://dx.doi.org/10.1016/s0968-090x(02)00032-3.

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Okhotnikov, V. I., M. N. Luchikhin, and M. A. Mamataliev. "INTELLIGENT TRANSPORT SYSTEMS IN VEHICLE TRAFFIC MANAGEMENT." Известия Кыргызского государственного технического университета им. И. Раззакова, no. 4 (2022): 479–82. http://dx.doi.org/10.56634/16948335.2022.4.479-482.

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John, A. Thompson. "AI-Integrated IoT Networks for Smart City Traffic Management." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 1–10. https://doi.org/10.5281/zenodo.15147275.

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The increase in rate of urban population and increasing urban infrastructure complexity have created humongous issues of traffic management. Conventional ways of urban traffic management are not efficient, creating traffic congestion, time delay, and adverse environmental effects. Smart traffic management of a smart city through integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is a paradigm-shift solution to all such issues. This article discusses how AI and IoT networks are converging to design adaptive, dynamic, and intelligent traffi

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Dr., Chukwurah Gladys Ogochukwu, Uchenna C. Chukwurah, and Success Chijioke. "Intelligent Transport Systems (ITS): An Approach to Traffic Congestion Challenges in Enugu Meteropolis." Journal of Transportation Systems 3, no. 3 (2018): 13–19. https://doi.org/10.5281/zenodo.2270195.

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Mobility is a necessity but the consequence is the resultant congestion from increase in traffic volume. At a time the solution was the construction of more transport infrastructures, but in the face of a persistent decrease in land other methods of tackling the ever increasing transport problems gave birth to systems such as Intelligent Transport Systems. This study attempts to assess the role of Intelligent Transport System in solving the problem of traffic congestion by identifying the existing system of traffic management in Enugu Urban and determining the potential for a more effective tr

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Holotiuk, Mykola, Viktoriia Doroshchuk, and Іvanna Berezhniak. "Modern Approaches to Traffic Management in Emergency Situations." Central Ukrainian Scientific Bulletin. Technical Sciences 1, no. 11(42) (2025): 280–86. https://doi.org/10.32515/2664-262x.2025.11(42).1.280-286.

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The article discusses modern approaches to the organization of traffic in emergency situations, which is an urgent issue for improving the safety and efficiency of transport infrastructure. The main focus is on the integration of innovative technologies, intelligent control systems and information and analytical tools to optimize traffic in crisis conditions. The study analyzes the existing methods of traffic management during emergencies, such as natural disasters, man-made accidents, and military threats. It is proposed to use three main mathematical models to evaluate the effectiveness of t

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Atassi, Reem, and Aditi Sharma. "Intelligent Traffic Management using IoT and Machine Learning." Journal of Intelligent Systems and Internet of Things 8, no. 2 (2023): 08–20. http://dx.doi.org/10.54216/jisiot.080201.

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The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows.

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Kazembe, Mangani Daudi. "Intelligent Transport Systems." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 488–92. http://dx.doi.org/10.22214/ijraset.2022.45271.

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Abstract: Growing economic activities aand others are among the factors that make many cities busy places today especially in traffic systems. Road networks that seem to be spacious sometimes become completely congested so much so that traffic mobility looks standstill. This impacts negatively on traffic users resources in terms of time management, fuel and other resource. This paper discusses the background trend in traffic activities amid congestion environment and proposes an Intelligent Traffic System that uses Machine Learning technique of predictive classification and regression to help

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Palla, Tharun. "Intelligent Traffic Management Using Big Data Analytics and IOT." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1495–503. http://dx.doi.org/10.22214/ijraset.2021.38650.

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Abstract: With rapid growth in personal luxury and increasing jobs, People are comfortable using their personal vehicles rather than public transport to fulfill their transportation needs. This is because of ease of access and feasibility to use the vehicles at their own will at any point of time. It is leading to heavy traffic congestions and long waiting periods at traffic signals which is becoming a heavy burden in all major cities and will be affecting environment because of pollution caused by so many vehicles and also will disturb the individual’s time schedule. This paper proposes a sys

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Ravish, Roopa, and Shanta Ranga Swamy. "Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives." Transport and Telecommunication Journal 22, no. 2 (2021): 163–82. http://dx.doi.org/10.2478/ttj-2021-0013.

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Abstract Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and co

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