Relevant bibliographies by topics / Intelligent traffic system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent traffic system'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Intelligent traffic system"

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Mohanty, Anita, Urmila Bhanja, and Sudipta Mahapatra. "Intelligent Traffic Quantification System." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012179. http://dx.doi.org/10.1088/1757-899x/225/1/012179.

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T M, Inba Malar, Bharatha Sreeja G, Amala Justus Selvam M, Jemima Sharon E, Jeevitha K, Keerthi R, and Mahalashmi R. "Intelligent Traffic Control System Using Deep Learning." ECS Transactions 107, no. 1 (April 24, 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, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.
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Shastry, Swathi, and T. R. Naveenkumar. "Routing of Traffic Sensors in Intelligent Transportation System." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 98–103. http://dx.doi.org/10.9756/bijsesc.8252.

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Satpute, Ms Bhumika Vasant, Ms Dhanlaxmi Balavant Don, Ms Rakhi Ajaykumar Salave, Ms Abrar Zameer Shaikh, and Prof Akash K. Gunjal. "Intelligent Transportation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4560–69. http://dx.doi.org/10.22214/ijraset.2022.43354.

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Abstract: People have experienced frequent communication and information exchange in recent years as a result of the proliferation of mobile devices. For example, when people go on vacations, it is common for each person to bring a smart phone with them to get information about nearby attractions. When a user visits a location, the application will provide useful information based on the user's current location preferences and previous visits to locations and their traffic signs. This new feature of map will learn your preferences and will display traffic signs in the area this system would display all traffic signs in and around the city including No Parking, Give Way, Speed Breakers ,Zebra Crossings ,Signals ,Tunnels, Sharp Curves, Speed ,No Overtaking Zones, Accidents Ponds, and Cycle Lanes. The use of popularity based filtering allows users to see all of the traffic signs in the area. Keywords: Traffic signs, Intelligent Transportation
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KANAKAM, VYSHNAVI, and RAGHUNATH SWAPNA. "INTELLIGENT TRAFFIC LIGHT CONTROL SYSTEM." i-manager’s Journal on Image Processing 8, no. 2 (2021): 9. http://dx.doi.org/10.26634/jip.8.2.18188.

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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 theoretical and applied problems in the field of traffic management, transport modelling and planning.
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Agarwal, Bikash, Sanidhya Rasiwasi, Shyam Agarwal, and Roshan Kumar. "Intelligent Highway Control System for Managing Traffic on Highway Intersections." International Journal of Engineering and Technology 4, no. 2 (2012): 153–57. http://dx.doi.org/10.7763/ijet.2012.v4.338.

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Faraj, Mohammed Abdulmaged, and Najmadin Wahid Boskany. "Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network." UHD Journal of Science and Technology 4, no. 2 (December 10, 2020): 123–31. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp123-131.

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Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.
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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 (September 27, 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 attention of many researchers and can process large amounts of data that are suitable for solving complex real-world problems about traffic control. In this paper, we worked on the real-world scenario of Shiraz City, which currently does not use any intelligent method and works based on fixed-time traffic signal scheduling. We applied IoT approaches and AI techniques to control traffic lights more efficiently, which is an essential part of the ITS. Specifically, sensors such as surveillance cameras were used to capture real-time traffic information for the intelligent traffic signal control system. In fact, an intelligent traffic signal control system is provided by utilizing distributed Multi-Agent Reinforcement Learning (MARL) and applying the traffic data of adjacent intersections along with local information. By using MARL, our goal was to improve the overall traffic of six signalized junctions of Shiraz City in Iran. We conducted numerical simulations for two synthetic intersections by simulated data and for a real-world map of Shiraz City with real-world traffic data received from the transportation and municipality traffic organization and compared it with the traditional system running in Shiraz. The simulation results show that our proposed approach performs more efficiently than the fixed-time traffic signal control scheduling implemented in Shiraz in terms of average vehicle queue lengths and waiting times at intersections.
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Li, Chao, Hong Wei Ding, Qian Lin Liu, and Xu Lu. "The Application of Polling System in Intelligent Transportation System." Applied Mechanics and Materials 644-650 (September 2014): 823–27. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.823.

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With the advancement of society, transport plays an increasingly important role in human society, but more and more traffic problems are constantly plagued mankind. Aiming at the problems facing the traffic and improve the traffic situation, the polling system is applied to intelligent traffic control system in this paper. Based on the type of paper feed of the arrival rate theory, the vehicle arrives at different rates, the wait time is different. Arrival rate is high, the longer the waiting time. Then we simulate system and get the simulation results, and compare the theoretical and simulation results. Finally, the theory to practical systems is using in microprocessor hardware simulation, and actual results further validate the correctness. Through the intelligent traffic control system, a good solution to traffic jam phenomenon is got and traffic management plays an important role.
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Dissertations / Theses on the topic "Intelligent traffic system"

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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 tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.
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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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Feng, Jingwen. "Traffic Sign Detection and Recognition System for Intelligent Vehicles." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31449.

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Road traffic signs provide instructions, warning information, to regulate driver behavior. In addition, these signs provide a reliable guarantee for safe and convenient driving. The Traffic Sign Detection and Recognition (TSDR) system is one of the primary applications for Advanced Driver Assistance Systems (ADAS). TSDR has obtained a great deal of attention over the recent years. But, it is still a challenging field of image processing. In this thesis, we first created our own dataset for North American Traffic Signs, which is still being updated. We then decided to choose Histogram Orientation Gradients (HOG) and Support Vector Machines (SVMs) to build our system after comparing them with some other techniques. For better results, we tested different HOG parameters to find the best combination. After this, we developed a TSDR system using HOG, SVM and our new color information extraction algorithm. To reduce time-consumption, we used the Maximally Stable Extremal Region (MSER) to replace the HOG and SVM detection stage. In addition, we developed a new approach based on Global Positioning System (GPS) information other than image processing. At last, we tested these three systems; the results show that all of them can recognize traffic signs with a good accuracy rate. The MSER based system is faster than the one using only HOG and SVM; and, the GPS based system is even faster than the MSER based system.
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Magaia, Lourenço Lázaro. "A video-based traffic monitoring system /." Link to the online version, 2006. http://hdl.handle.net/10019.1/1243.

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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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Wigetman, Robert. "Vers la conception du système Sieel : un tuteur intelligent pour le contrôle aérien." Toulouse 3, 1994. http://www.theses.fr/1994TOU30258.

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Nous decrivons dans cette these les travaux de conception et de developpement d'un tuteur intelligent pour le controle aerien. Celui-ci comprend trois modules: un simulateur de trafic aerien et son interface homme machine, un systeme expert de controle aerien, et un systeme expert pedagogue. Nos travaux sont centres sur ce dernier module qui a ete concu de facon a etre aussi independant que possible du domaine du controle aerien, et ainsi de pouvoir s'appliquer a d'autres domaines du meme type. Pour ce faire, nous avons caracterise le travail du controleur par des strategies de planification d'actions qu'il doit mettre en uvre pour resoudre des problemes a la fois complexes et dynamiques auxquels il est confronte. A partir de cette caracterisation, nous avons concu une structure, en forme de graphe, pour rendre explicite les relations entre les connaissances utilisees. Ce graphe de connaissances est la fondation sur laquelle est bati notre systeme. Apres une introduction au domaine du controle aerien, nous presentons des generalites sur les tuteurs intelligents et decrivons quelques uns de ces systemes. Puis, nous presentons l'ensemble du systeme sieel de l'ecole nationale de l'aviation civile. Vient par la suite la description de nos travaux dans le detail: tout d'abord, l'analyse de la semantique des communications entre l'eleve et les agents simules a eu pour resultat l'implantation d'une grammaire de cas. Ensuite, la structuration des connaissances enseignees a permis l'elaboration d'un modele d'eleve a couverture partielle. Enfin, notre tuteur utilise ces elements pour comparer les actions de l'eleve avec les plans hypothetiques produits par un systeme expert du domaine afin d'evaluer son niveau d'expertise et de pouvoir determiner des interventions de guidage a mettre en uvre
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Magaia, Lourenco Lazaro. "A video-based traffic monitoring system." Thesis, Stellenbosch : University of Stellenbosch, 2006. http://hdl.handle.net/10019.1/1243.

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Thesis (PhD (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006.
This thesis addresses the problem of bulding a video-based traffic monitoring system. We employ clustering, trackiing and three-dimensional reconstruction of moving objects over a long image sequence. We present an algorithms that robustly recovers the motion and reconstructs three-dimensional shapes from a sequence of video images, Magaia et al [91]. The problem ...
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Dongkai, Yang, Bai Xin, and Zhang Qishan. "VEHICLE MONITORING SYSTEM FOR PUBLIC TRAFFIC IN BEIJING." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/607294.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada
With the rapid development of urban economy, there are bus increasing, route extending, and shuttle frequency increasing etc. At the same time, road construction is subject to land surface, so traffic jam often occurs. It is a big trouble for life of citizens and problem for economy development. So it needs to be improved as fast as possible. Vehicle monitoring system for public traffic in Beijing can expediently monitor the state of each controlled bus, thereby making perfect management. With the integration of GPS, analog trunked communication and digital map, the old, blinding manage system of public traffic would be changed into advanced, visualized management mode, and several routes are dispatched in one dispatch center at the same time. The system frame and its components are introduced in this paper.
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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 distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.
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Shil, Manash. "Designing and simulating a Car2X communication system using the example of an intelligent traffic sign." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-161679.

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The thesis with the title “Designing and simulating a Car2X communication system using the example of an intelligent traffic sign” has been done in Chemnitz University of Technology in the faculty of Computer Science. The purpose of this thesis is to define a layered architecture for Infrastructure to Vehicle (I2V) communication and the implementation of a sample intelligent traffic sign (variable speed limit) application for a Car2X communication system. The layered architecture of this thesis is defined based on three related projects. The application is implemented using the defined layered architecture. Considering the availability of hardware, the implementation is done using the network simulator OMNET++. To check the feasibility of the application three scenarios are created and integrated with the application. The evaluation is done based on the result log files of the simulation which show that the achieved results conform with the expected results, except some minor limitations.
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Books on the topic "Intelligent traffic system"

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Guerra, Tomás. ITS traffic data consolidation system. Phoenix, AZ: ADOT, 2005.

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

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Dailey, Daniel J. Deployment of a virtual sensor system, based on transit probes, in an operational traffic management system. Olympia, WA: Washington State Dept. of Transportation, 2006.

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Pegues, John Adam. The role of smart traffic centers in regional system operations: A Hampton Roads case study. Charlottesville, Va: Virginia Transportation Research Council, 2005.

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Congress, Nita. The automated highway system: An idea whose time has come. [Washington, D.C.?]: U.S. Dept. of Transportation, Federal Highway Administration, 1994.

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Kikō, Nihon Bōeki Shinkō. China probe traffic information communication system: Pilot demonstration project program to improve trade and investment environments (FY2005). [Tokyo]: Ministry of Economy, Trade and Industry, 2006.

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Haeyangbu, Korea (South) Kukt'o. Chinŭnghyŏng kyot'ong ch'egye kibon kyehoek 2020: Yuk, hae, kong t'onghap kyot'ong ch'egye chinŭnghwa kyehoek ('11-'20) = Intelligent transportation system. Kyŏnggi-do Kwach'ŏn-si: Kukt'o Haeyangbu, 2011.

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Fundamentals of traffic simulation. New York: Springer, 2010.

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Bebenik, Kevin. Intelligent transportation systems and road capacity. Ottawa: Transportation Association of Canada = Association des transports du Canada, 1999.

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Bazzan, Ana L. C., and Franziska Klügl. Introduction to Intelligent Systems in Traffic and Transportation. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01565-6.

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Book chapters on the topic "Intelligent traffic system"

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Biswas, Satya Priya, Paromita Roy, Nivedita Patra, Amartya Mukherjee, and Nilanjan Dey. "Intelligent Traffic Monitoring System." In Advances in Intelligent Systems and Computing, 535–45. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2523-2_52.

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Rahul Kumar and Kunal Gupta. "ITMS (Intelligent Traffic Management System)." In Advances in Intelligent Systems and Computing, 487–95. Singapore: 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, 17–28. Boca Raton: 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, 489–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9293-5_45.

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Tang, Nam, Cuong Do, Tien Ba Dinh, and Thang Ba Dinh. "Urban Traffic Monitoring System." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 573–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25944-9_74.

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Zhang, Xiubin, and Muhammad Mansoor Khan. "Intelligent Vehicle Navigation and Traffic System." In Principles of Intelligent Automobiles, 175–209. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2484-0_5.

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Sai Venu Prathap, K., D. Srinivasulu Reddy, S. Madhusudhan, and S. Mohammed Mazharr. "Intelligent Traffic Light System Using YOLO." In Algorithms for Intelligent Systems, 95–107. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_9.

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Bansal, Subodh, and Amit Gupta. "IoT-Enabled Intelligent Traffic Management System." In IoT Based Smart Applications, 89–111. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04524-0_6.

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Wang, Rongxia. "Intelligent Traffic System Path Planning Algorithm." In Advances in Intelligent Systems and Computing, 326–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43306-2_46.

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Ravish, Roopa, Datthesh P. Shenoy, and Shanta Rangaswamy. "Sensor-Based Traffic Control System." In Advances in Intelligent Systems and Computing, 207–21. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2188-1_17.

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Conference papers on the topic "Intelligent traffic system"

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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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Bilal, Jubair Mohammed, and Don Jacob. "Intelligent Traffic Control System." In 2007 IEEE International Conference on Signal Processing and Communications. IEEE, 2007. http://dx.doi.org/10.1109/icspc.2007.4728364.

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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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Manikonda, Prithvinath, Anil Kumar Yerrapragada, and Sai Sasank Annasamudram. "Intelligent traffic management system." In 2011 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT). IEEE, 2011. http://dx.doi.org/10.1109/student.2011.6089337.

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Lakshmi, Ch Jaya, and S. Kalpana. "Intelligent traffic signaling system." In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2017. http://dx.doi.org/10.1109/icicct.2017.7975196.

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Bansal, Tarun, Taher Ali Rangwala, and Mahesh Agrawal. "Intelligent Traffic Management System." In Proceedings of the Student Research Symposium (SRS'13). Singapore: Research Publishing Services, 2013. http://dx.doi.org/10.3850/978-981-07-7393-9_043.

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Spoorthi, P. N., and S. D. Yashwanth. "Intelligent Traffic Management System." In 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT). IEEE, 2021. http://dx.doi.org/10.1109/iceeccot52851.2021.9707986.

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Saleh, Aneesa, Steve A. Adeshina, Ahmad Galadima, and Okechukwu Ugweje. "An intelligent traffic control system." In 2017 13th International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2017. http://dx.doi.org/10.1109/icecco.2017.8333313.

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Priyadarshi, Sachin, Shivam Shekhar, Trinesh Kumar Singh, Shivam Kumar Gupta, Mohit Bansal, Jay Singh, and Kailash Sharma. "Intelligent Traffic Control System (ITCS)." In 2018 International Conference on Sustainable Energy, Electronics, and Computing Systems (SEEMS). IEEE, 2018. http://dx.doi.org/10.1109/seems.2018.8687368.

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Pyykonen, P., J. Laitinen, J. Viitanen, P. Eloranta, and T. Korhonen. "IoT for intelligent traffic system." In 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2013. http://dx.doi.org/10.1109/iccp.2013.6646104.

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Reports on the topic "Intelligent traffic system"

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Tayeb, Shahab. Protecting Our Community from the Hidden Vulnerabilities of Today’s Intelligent Transportation Systems. Mineta Transportation Institute, May 2022. http://dx.doi.org/10.31979/mti.2022.2132.

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Abstract:
The ever-evolving technology interwoven into the transportation industry leaves it frequently at risk for cyber-attacks. This study analyzes the security of a common in-vehicle network, the Controller Area Network (CAN), standard in most vehicles being manufactured today. Like many other networks, CAN comes with inherent vulnerabilities that leave CAN implementations at risk of being targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the network. The research team initially performed a simulation of CAN traffic generation followed by hardware implementation of the same on a test vehicle. Due to the concealed and untransparent nature of CAN, the team reverse-engineered the missing parameters through a series of passive "sniffing attacks" (attacks using data reading utilities called packet sniffers) on the network and then demonstrated the feasibility of the attack by placing fabricated frames on the CAN.
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Accelerating the tactical decision process leverages capabilities in three technology areas: (1) High-Performance Computing (HPC), (2) Machine Learning (ML), and (3) Internet of Things (IoT). Exploiting these areas can reduce network traffic and shorten the time required to transform data into actionable information. Faster decision cycles may revolutionize battlefield operations. Presented is an overview of an artificial intelligence (AI) system design for near-real-time analytics in a tactical operational environment executing on co-located, mobile HPC hardware. The report contains the following sections, (1) an introduction describing motivation, background, and state of technology, (2) descriptions of tactical decision process leveraging HPC problem definition and use case, and (3) HPC tactical data analytics framework design enabling data to decisions.
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