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Relevant bibliographies by topics / Intelligent traffic management

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Academic literature on the topic 'Intelligent traffic management'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Intelligent traffic management"

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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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Dissertations / Theses on the topic "Intelligent traffic management"

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Azimian, Amin. "Design of an Intelligent Traffic Management System." University of Dayton / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1323275800.

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Gu, Wei. "Dynamic network traffic management, approaching with intelligent switching." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62744.pdf.

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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.

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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be

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Tuul, Viktor, and John Dahlberg. "Intelligent Traffic Intersection Management Using Motion Planning for Autonomous Vehicles." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214714.

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With the increasing advances in the field of autonomousvehicles it is alluring to ask if a possible vehicularparadigm shift is in the near future. Maximizing road capacitywith Intelligent Traffic Intersections that communicate withautonomous vehicles could become a reality, where the needfor traffic lights and stop signs is excluded. In this paper, anAutonomous Intersection Management system is introduced thatutilizes trajectory-based prioritization and motion planning techniquesto manage traffic in an orthogonal single lane four-wayintersection. The developed system reduces the need for vehic

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Cook, Brandon M. "An Intelligent System for Small Unmanned Aerial Vehicle Traffic Management." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617106257481515.

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Dahlberg, John Henry, and Viktor Tuul. "Intelligent Traffic Intersection Management Using Motion Planning for Autonomous Vehicles." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210895.

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With the increasing advances in the field of autonomousvehicles it is alluring to ask if a possible vehicularparadigm shift is in the near future. Maximizing road capacitywith Intelligent Traffic Intersections that communicate withautonomous vehicles could become a reality, where the needfor traffic lights and stop signs is excluded. In this paper, anAutonomous Intersection Management system is introduced thatutilizes trajectory-based prioritization and motion planning techniquesto manage traffic in an orthogonal single lane four-wayintersection. The developed system reduces the need for vehic

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Chowdhury, Abdullahi. "Enhancing service quality and reliability in intelligent traffic system." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173723.

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Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in

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Tian, Yi. "Self-Powered Intelligent Traffic Monitoring Using IR Lidar and Camera." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/74949.

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This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusi

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Hamza-Lup, Georgiana. "SENSOR-BASED COMPUTING TECHNIQUES FOR REAL-TIME TRAFFIC EVACUATION MANAGEMENT." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3477.

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The threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will occur. This lack of information posses great challenges on those responsible for security, specifically, o

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Jaworski, P. "Cloud computing based adaptive traffic control and management." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/d63ba84e-bd0c-4e00-8242-310dbbaa3b92/1.

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Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control dis

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Books on the topic "Intelligent traffic management"

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ITS Council (Institute of Transportation Engineers). Committee on Management and Operations. Management and operations of intelligent transportation systems. Institute of Transportation Engineers, 1999.

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Engineers, Institute of Transportation, ed. Management and operations of intelligent transportation systems. Institute of Transportation Engineers, 2008.

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Ishimaru, John M. North Seattle advanced traffic management system (NSATMS) project evaluation. Washington State Dept. of Transportation, 2002.

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United States. Joint Program Office for Intelligent Transportation Systems, ed. Intelligent Transportation Systems field operational test cross-cutting study: Incident management : detection, verification, and traffic management. ITS Joint Program Office, 1998.

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Gordon, Robert L. Intelligent freeway transportation systems: Functional design. Springer, 2009.

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United States. Joint Program Office for Intelligent Transportation Systems., ed. Developing freeway and incident management systems using the national ITS architecture. U.S. Dept. of Transportation, ITS Joint Program Office, 1998.

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Dailey, Daniel J. TrafficTV. Washington State Dept. of Transportation, 2005.

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Dunn, Walter M. Roadway incident diversion practices. National Academy Press, 1999.

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J, DeBlasio Allan, United States. Joint Program Office for Intelligent Transportation Systems., and John A. Volpe National Transportation Systems Center (U.S.), eds. A review of metropolitan area early deployment plans and congestion management systems for the development of intelligent transportation systems. U.S. Dept. of Transportation, Research and Special Programs Administration, John A. Volpe National Transportation Systems Center, 1997.

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Dailey, Daniel J. TrafficTV, phase 2. Washington State Dept. of Transportation, 2006.

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Book chapters on the topic "Intelligent traffic management"

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Amador, António, Rui Dias, Tiago Dias, and Tomé Canas. "Traffic Management Systems." In Intelligent Transport Systems. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118894774.ch13.

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Gordon, Robert. "Active Traffic Management (ATM)." In Intelligent Transportation Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14768-0_10.

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Rahul Kumar and Kunal Gupta. "ITMS (Intelligent Traffic Management System)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_40.

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Gowtham, M., M. K. Banga, Mallanagouda Patil, and Natarajan Meghanathan. "An Intelligent Traffic Management System." In Cyber-Physical Systems and Industry 4.0. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003129790-2.

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Jahnavi, Somavarapu, G. Prasanth, D. Priyanka, A. Sneheth, and M. Navya. "Intelligent Traffic Light Management System." In Learning and Analytics in Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9293-5_45.

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Sureshkumar, P., J. Niresh, S. Tamilselvi, and Baskar Duraisamy. "Intelligent Transportation and Traffic Management." In The Intersection of 6G, AI/Machine Learning, and Embedded Systems. CRC Press, 2025. https://doi.org/10.1201/9781003540212-14.

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Temme, Annette, Ingrid Gerdes, and Roland Winkler. "Computational Intelligence in Air Traffic Management." In Computational Intelligence in Intelligent Data Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32378-2_20.

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Bermejo, A. J., J. Villadangos, J. J. Astrain, and A. Córdoba. "Ontology Based Road Traffic Management." In Intelligent Distributed Computing VI. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32524-3_14.

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ZHANG, Cunbao, Feng CHEN, Ruiyang QIN, Xuemei LI, and Houyi WANG. "Intelligent Traffic Management and Control Technology." In Intelligent Road Transport Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5776-4_7.

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Bansal, Subodh, and Amit Gupta. "IoT-Enabled Intelligent Traffic Management System." In IoT Based Smart Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04524-0_6.

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Conference papers on the topic "Intelligent traffic management"

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S, Siva Rama Krishnan, Siva Sankaran G, Samrith U, Abdul Samath A, and R. Manjula Devi. "Intelligent Traffic Signal Management System." In 2025 Emerging Technologies for Intelligent Systems (ETIS). IEEE, 2025. https://doi.org/10.1109/etis64005.2025.10960850.

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A, Vasumathi, Dharshini P, Jagath M, Krishna Raj R, and Mahalakshmi S. "Intelligent Traffic Management using Machine Learning." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985641.

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C, Joesam Dinesh, Shrinidhi S, Sibi Amaran, Sree Kumar K, and Karthikeyan U. "YOLO-based Traffic Signal Optimization for Intelligent Traffic Flow Management." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714806.

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Mishra, Awakash, Monika Abrol, and Pooja Varma. "Intelligent Traffic Management System using Vehicular Networks." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774897.

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Sateesh, Rayala, Manoj Kumar, P. Venu, and Varun Shanagapati. "Smart Traffic Management System." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828951.

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Du, Junlong, Xiaoni Wang, Qiuxin Wu, and Can Wang. "Blockchain-based Federated Learning System for Intelligent Traffic Management." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667585.

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Shaheen, Dalaali, Getzi Jeba Leelipushpam Paulraj, Immanuel Johnraja Jebadurai, and Stewart Kirubakaran. "Intelligent Traffic Management System using Multi-Agent Reinforcement Learning." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10941365.

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Antl, Martin, Emília Pietriková, and Peter Poor. "Intelligent Urban Traffic Management Systems – Takeways from Martin City." In 2024 IEEE 17th International Scientific Conference on Informatics (Informatics). IEEE, 2024. https://doi.org/10.1109/informatics62280.2024.10900814.

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Hu, Dian-xia, and Zhi-peng Zheng. "Intelligent traffic management system." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6011360.

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Alshaer, Jawdat J., and Vasily V. Gubarev. "Intelligent traffic management system." In 2009 International Siberian Conference on Control and Communications (SIBCON 2009). IEEE, 2009. http://dx.doi.org/10.1109/sibcon.2009.5044823.

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Reports on the topic "Intelligent traffic management"

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Tayeb, Shahab. Intelligent Blind Crossings for Suburban and Rural Intersections. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2024.2351.

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Blind intersections in suburban and rural areas pose significant safety challenges due to limited visibility and inadequate infrastructure. This project proposes an innovative solution leveraging the Internet of Vehicles (IoV) paradigm, utilizing connected and autonomous vehicles (CAVs) for seamless communication to enhance safety at these intersections. The research focuses on developing a specialized Road-Side Unit (RSU) system equipped with a Virtual Traffic Light Algorithm implemented on a Field-Programmable Gate Array (FPGA). Key stakeholders, including transportation authorities, vehicle

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Yaseen, Lama, Nourah Al-Hosain, Ibrahem Shatnawi, and Abdelrahman Muhsen. Impact of Urban Traffic on Fuel Consumption Leveraging IoT Data: Case Study of Riyadh City. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp72.

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This study explores the rising trend of traffic congestion in Riyadh and its impact on fuel consumption for passenger cars amid the challenges of rapid urbanization and increasing vehicle use. By utilizing real-time floating car data (FCD) collected by vehicles equipped with Global Positioning System (GPS) technology and communication systems, this study illustrates the potential of the Internet of Things (IoT) and smart city technologies in developing intelligent transportation systems and improving urban mobility management. A spatial analysis of the traffic flow dynamics in Riyadh, focusing

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Deng, Zhuohang, Zhiliang Luo, Neil Hockaday, Ahmed Farid, and Anurag Pande. Evaluation of Left Shoulder as Part-Time Travel Lane Design Alternatives and Transportation Management Center Staff Training Module Development. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2153.

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Permanent capacity expansion, such as adding new lanes, is no longer a viable strategy to address traffic congestion in California; hence, ITS (Intelligent Transportation System) strategies, such as part-time use of the shoulder as a travel lane, need to be explored. The use of the shoulder as a travel lane during peak traffic hours has limited applications in the US, and most use the right shoulder as a part-time travel lane even though either the right or left shoulder (but not both) may be used. Caltrans District 5 is exploring the use of Left Shoulder as a Part-time Travel Lane (LSPTTL) as

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Pourhomayoun, Mohammad. Automatic Traffic Monitoring and Management for Pedestrian and Cyclist Safety Using Deep Learning and Artificial Intelligence. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1808.

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum

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