Relevant bibliographies by topics / Traffic surveillance system

Contents

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

Academic literature on the topic 'Traffic surveillance system'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Traffic surveillance system"

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Ishwarya, M. V., D. Deepa, S. Hemalatha, A. Venkata Sai Nynesh, and A. Prudhvi Tej. "Gridlock Surveillance and Management System." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3281–84. http://dx.doi.org/10.1166/jctn.2019.8177.

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In today’s world, the traffic problems are high because of more use of vehicles in the cities. So because of this, it is impossible to give away for an emergency vehicle like, (Ambulances, police vehicles, fire engine etc.). So with this project, we are going to reduce this problem, for this, we have created a system that will monitor the traffic and also gives a way to emergency vehicles. We calculate the current traffic rate with normal traffic rate and if there is any unusual traffic and also if there is an emergency vehicle is passing through that way we change the signal lights into green to give away for the emergency vehicles. And if there is any heavy traffic for a long time and it does not control then our system will send a message to the driver that u have to take another route. By this way we can reduce the traffic problems and emergency vehicles will reach their destination in time.
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Tran, Duong Nguyen-Ngoc, Tien Phuoc Nguyen, Tai Nhu Do, and Synh Viet-Uyen Ha. "Subsequent Processing of Background Modeling for Traffic Surveillance System." International Journal of Computer Theory and Engineering 8, no. 3 (2016): 235–39. http://dx.doi.org/10.7763/ijcte.2016.v8.1050.

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Ganesan, K., C. Kavitha, Kriti Tandon, and R. Lakshmipriya. "Traffic Surveillance Video Management System." International journal of Multimedia & Its Applications 2, no. 4 (2010): 28–36. http://dx.doi.org/10.5121/ijma.2010.2403.

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Journal, IJSREM. "Advanced Drone Surveillance System for Traffic Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28534.

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The rapid urbanization and increasing vehicle traffic in metropolitan areas have given rise to a significant challenges in traffic management. Traditional methods of a traffic monitoring and control often struggle to cope with the dynamic and complex nature of urban traffic patterns. In this context, we propose an innovative Drone Surveillance System for Traffic Management to revolutionize the way traffic is monitored, analyzed, and optimized in urban environments. Our system leverages state-of-the-art drone technology equipped with advanced sensors, high-resolution cameras, and intelligent algorithms to provide real-time traffic insights. Drones are deployed strategically across key traffic arteries, allowing for comprehensive aerial coverage and data collection. The system focuses on several key objectives: real-time traffic monitoring, incident detection, traffic flow analysis, optimized traffic signal control, emergency response support, data-driven decision making, and ensuring privacy and security compliance. By the employing cutting-edge computer vision algorithms, machine learning techniques, and data analytics, the Drone Surveillance System processes vast amounts of traffic data with speed and precision. The system detects the traffic incidents and the such as accidents and road hazards promptly, enabling swift a responses from emergency services. It analyzes traffic patterns and predicts congestion, allowing for adaptive traffic signal control and dynamic route optimization, thereby minimizing congestion and reducing travel time for a commuters. One of the system’s notable features is its a ability to the support emergency response teams effectively. Drones identify incidents and relay on the critical information to emergency services, International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 02 | February - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28534 | Page 2 enabling rapid deployment and efficient management of emergencies. Moreover, the system ensures the privacy of individuals through strict adherence to regulations and deploys robust security measures to safeguard data integrity. An important advancement in the urban traffic control system is the Drone Surveillance System for Traffic control. The technology turns traditional traffic management into a proactive, flexible, and data-driven procedure by utilizing the power of drones, real-time data processing, and sophisticated algorithms. This innovation not only makes urban transportation more efficient, but it also helps to improve road safety by lowering accident rates, easing traffic, and creating a more sustainable urban environment. Keywords: Traffic Management, Real-time Monitoring, Incident Detection, Traffic Flow Analysis, Optimized Traffic Signal Control, Machine Learning Techniques. Drone Surveillance System
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Wasudeo P., Mr Rahane. "VIDEO SURVEILLANCE BASED TRAFFIC MONITORING SYSTEM." IOSR Journal of Engineering 02, no. 04 (2012): 538–43. http://dx.doi.org/10.9790/3021-0204538543.

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Abid, Ahmed, Ramzy Ali, and Rafah Saheb. "Vehicle Remote Support and Surveillance System." Iraqi Journal for Electrical and Electronic Engineering 10, no. 2 (2014): 55–63. http://dx.doi.org/10.37917/ijeee.10.2.1.

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The proposed design offers a complete solution to support and surveillance vehicles remotely. The offered algorithm allows a monitoring center to track vehicles; diagnoses fault remotely, control the traffic and control CO emission. The system is programmed to scan the on-board diagnostic OBD periodically or based on request to check if there are any faults and read all the available sensors, then make an early fault prediction based on the sensor readings, an experience with the vehicle type and fault history. It is so useful for people who are not familiar with fault diagnosis as well as the maintenance center. The system offers tracking the vehicle remotely, which protects it against theft and warn the driver if it exceeds the speed limit according to its location. Finally, it allows the user to report any traffic congestion and allows a vehicle navigator to be up to date with the traffic condition based on the other system’s user feedback.
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Ungaro, J. F., G. H. De Hoyos, and C. Ubeda. "Building a traffic accidents epidemiological surveillance system." Injury Prevention 16, Supplement 1 (2010): A216. http://dx.doi.org/10.1136/ip.2010.029215.770.

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Harinarayanan, N., and Dr K. Sudha. "Advanced Drone Surveillance System for Traffic Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28537.

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The growing need for sustainable and efficient construction practices has spurred a surge in the development of advanced building materials. Lightweight wall panels have emerged as a promising solution to address the challenges of traditional construction methods. This abstract delves into the design, composition, and benefits of lightweight wall panels, highlighting their contributions to sustainability, energy efficiency, and ease of installation. The lightweight wall panel comprises a combination of materials such as foam cores, composite materials, and reinforcements, carefully engineered to optimize strength while significantly reducing overall weight. This innovative construction material offers various advantages over conventional walls, including enhanced thermal and acoustic insulation properties, increased structural integrity, and improved resistance to fire and moisture. Furthermore, the streamlined manufacturing process of lightweight wall panels minimizes waste generation and reduces the carbon footprint associated with transportation and installation. These panels also facilitate quicker construction timelines, enabling cost-effectiveness and increased flexibility in building design. This abstract discusses the diverse applications of lightweight wall panels across residential, commercial, and industrial sectors, emphasizing their adaptability and versatility in meeting diverse construction requirements. Additionally, it explores ongoing research and developments in the field, aiming to further enhance the performance and sustainability aspects of these panels.
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Warnars, Harco Leslie Hendric Spits. "ONLINE TRANSPORTATION SURVEILLANCE SYSTEM FOR IMPROVING DRIVING BEHAVIOUR." Open Journal of Science and Technology 3, no. 4 (2020): 364–73. http://dx.doi.org/10.31580/ojst.v3i4.1607.

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Traffic is a problem in every nation. Indonesia is one of the most populous countries in Indonesia, certainly has such one of the biggest problems in the world, especially in big cities such as Jakarta. This is mostly contributed by the number of vehicles in Jakarta. Combine with current trends of Online transportation; the problem is getting bigger. Online transportation is on the rise in Indonesia. Sometimes seen as providing more benefits to people of Indonesia, online transportation also has its side-effects. As the number of online transportations rises, so is the level of traffic in big cities such as Jakarta. This traffic is also getting worse due to the bad driving behavior of Indonesian people, especially the online transportation driver. To better relieve traffic, a system to improve the drivers’ behavior can be developed to ensure safety, increasing driving behavior awareness, and hopefully decrease the traffic problem as a whole.
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Xiang, Zheng, Wei Jun Pan, and Jun Luo. "Application of Automatic Dependent Surveillance-Broadcast in the Training for Air Traffic Controller." Applied Mechanics and Materials 209-211 (October 2012): 775–78. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.775.

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The application of Automatic Dependent Surveillance-Broadcast (ADS-B) in the training for air traffic controller is investigated in the presented work. ADS-B is a surveillance technology for tracking aircraft as part of the Next Generation Air Transportation System (NextGen). Compared to radar, ADS-B system has many advantages such as lower deployment cost, higher accuracy and more frequently update. As surveillance method transits from radar surveillance to ADS-B surveillance, the training for air traffic controller should incorporate ADS-B system correspondingly. In the presented work, the features of ADS-B are introduced as well as the structure of ADS-B system is illustrated, and a novel framework for training air traffic controller with ADS-B system is proposed. It is seen that air traffic controller can be trained with lower cost, shorter period and more flexibility compared to that with radar surveillance.
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Dissertations / Theses on the topic "Traffic surveillance system"

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Hunt, Daniel, and Magnus Olsson. "ATSVIS : Airport Traffic Surveillance and Visual Information System." Thesis, Mälardalen University, Department of Mathematics and Physics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-259.

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<p>Det primära målet med examensarbetet var att utforma ett koncept på hur man kan göra flygtrafiken säkrare både på marken och i luften. Konceptet fick namnet ATSVIS, vilket står Airport Traffic Surveillance and Visual Information System. ATSVIS är tänkt att ge anvisningar om vad nya idéer och ny teknologi kan tillföra piloten och trafikledningen. ATSVIS avser även att automatisera och förenkla flygtrafiken, vilket skall leda till säkrare flygtrafik både i luften och på marken. Konceptet ATSVIS kommer att utformas efter egna idéer och från intervjuer med branschfolk. Huvudarbetet av examensarbetet kommer att vara att ta fram en simulator där man kan visa och pröva hur ATSVIS fungerar.</p><br><p>The primary objective with the thesis is to formulate a concept on how to do the air traffic safer, both on the land and in the air. The concept got the name ATSVIS, which stands for Airport Traffic Surveillance and Visual Information System. ATSVIS is intended to give instructions what new ideas and new technology can do, to allocate the pilot and the traffic management. ATSVIS also intend to automate and to simplify the air traffic. The concept is formulated after own ideas and from interviews with people in the flight industry. The central work of the thesis is a simulator in which one can show and to examine how ATSVIS functions work.</p>
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Graham, Robert Lewis. "Evaluation of a computer vision traffic surveillance system." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1202501465/.

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Luo, Ning. "A Wireless Traffic Surveillance System Using Video Analytics." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68005/.

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Video surveillance systems have been commonly used in transportation systems to support traffic monitoring, speed estimation, and incident detection. However, there are several challenges in developing and deploying such systems, including high development and maintenance costs, bandwidth bottleneck for long range link, and lack of advanced analytics. In this thesis, I leverage current wireless, video camera, and analytics technologies, and present a wireless traffic monitoring system. I first present an overview of the system. Then I describe the site investigation and several test links with different hardware/software configurations to demonstrate the effectiveness of the system. The system development process was documented to provide guidelines for future development. Furthermore, I propose a novel speed-estimation analytics algorithm that takes into consideration roads with slope angles. I prove the correctness of the algorithm theoretically, and validate the effectiveness of the algorithm experimentally. The experimental results on both synthetic and real dataset show that the algorithm is more accurate than the baseline algorithm 80% of the time. On average the accuracy improvement of speed estimation is over 3.7% even for very small slope angles.
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Ambardekar, Amol A. "Efficient vehicle tracking and classification for an automated traffic surveillance system." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1451111.

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Lorio, Berino. "Towards a non-intrusive traffic surveillance system using digital image processing." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52589.

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Thesis (MScEng)--Stellenbosch University, 2001.<br>ENGLISH ABSTRACT: With the increased focus on the use of innovative and state-of-the-art technology in Intelligent Transport Systems (ITS), the need for more accurate and more detailed road traffic flow data has become apparent. Data obtained from vehicle detector loops, which merely act as vehicle presence sensors, is neither reliable nor accurate enough anymore. This type of sensor poses the problem that it has to be inserted into the road surface; temporarily obstructing traffic flows, and has to be replaced after pavement reconstruction. One of the solutions to this problem is to develop a traffic surveillance system that uses video image processing. In cities where Intelligent Transport Systems are used extensively, roadways are monitored through Closed Circuit Television Cameras (CCTV) that are closely watched by traffic control centre personnel. These cameras are mounted on posts on the roadside. These cameras can serve a dual purpose, being used for both human monitoring and as inputs to Video Image Processing Systems. In this study some of the digital image processing techniques that could be used in a traffic surveillance system were investigated. This report leads the reader through the various steps in the processing of a scene by a traffic surveillance system based on feature tracking, and discusses the pitfalls and problems that are experienced. The tracker was tested using three image sequences and the results are presented in the final chapter of this report.<br>AFRIKAANSE OPSOMMING: Met die toenemende fokus op die gebruik van innoverende oplossings en gevorderde tegnologie in Intelligente Vervoerstelsels, het die noodsaaklikheid van akkurater en meer gedetailleerde padverkeer vloeidata duidelik geword. Data wat verkry word d.m.v. voertuig deteksie lusse, wat alleenlik voertuig teenwoordigheid/afwesigheid meet, is nie meer akkuraat of betroubaar genoeg nie. Hierdie tipe sensors het egter die nadeel dat dit in die plaveisel ingesny moet word, dus vloei tydelik kan belemmer, en moet vervang word elke keer as plaveisel rekonstruksie gedoen word. Een van die oplossings vir hierdie probleem is om 'n verkeers waarnemingstelsel te ontwikkel wat van videobeeldverwerking gebruik maak. In stede waar van uitgebreide intelligente verkeerstelsels gebruik gemaak word, word paaie gemonitor d.m.v. geslote baan televisiekameras wat op pale langs die paaie aangebring is. Personeellede van die verkeers beheer sentrum hou dan die inkomende televisiebeelde dop. Hierdie kameras kan 'n dubelle rol vervul deurdat dit vir beide menslike waarneming en as invoer in 'n video-beeldverwerking stelsel gebruik kan word. In hierdie studie was verskeie digitale beeldverwerking tegnieke wat gebruik kan word in 'n verkeers waarnemingstelsel ondersoek. Hierdie verslag lei die leser deur die verskeie stappe in die verwerking van 'n toneel deur 'n verkeers waarneming stelsel wat gebaseer is op die volg van kenmerke. Die verslag beskryf ook die slaggate en probleme wat ondervind word. Die voertuig volger was getoets deur van drie reekse beelde gebruik te maak en die resultate word weergegee in die finale hoodfstuk van hierdie verslag.
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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.<br>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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HASE, Hiroyuki, Toyohide WATANABE, and Jien KATO. "A Highway Surveillance System Using an HMM-Based Segmentation Method." Institute of Electronics, Information and Communication Engineers, 2002. http://hdl.handle.net/2237/14983.

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Samples, Agnes Mary Banks. "Validity of Self-Reported Data on Seat Belt Use: The Behavioral Risk Factor Surveillance System." [Johnson City, Tenn. : East Tennessee State University], 2004. http://etd-submit.etsu.edu/etd/theses/available/etd-0315104-172201/unrestricted/SamplesA032604f.pdf.

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Thesis (Ed. D.)--East Tennessee State University, 2004.<br>Title from electronic submission form. ETSU ETD database URN: etd-0315104-172201. Includes bibliographical references. Also available via Internet at the UMI web site.
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Norén, Bång Ola. "STCA : an aircraft conflict alert system." Thesis, Linköping University, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2297.

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<p>The purpose of this Master’s Thesis is to produce a specification for the aircraft conflict alert system STCA, and implement a prototype as a module in the air traffic surveillance system NOVA9000. </p><p>The specification is constructed based on functional requirements from EUROCONTROL and describes a system using a nominal trajectory method, where the future paths of aircraft are estimated. The trajectory is created using a probabilistic approach, where future positions are described with probability fields. </p><p>The prototype is implemented using the specification with some simplifications. The prototype is evaluated using recorded traffic from a heavy air traffic region surrounding an airport with parallel runways. 15 alerts were induced in 1,5 hour of morning traffic; this is far too much to be acceptable. Improvements are proposed and explanations to the high rate of alerts are made.</p>
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Nguyen, Van Duong. "Variational deep learning for time series modelling and analysis : applications to dynamical system identification and maritime traffic anomaly detection." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0227.

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Ce travail de thèse se focalise sur une classe de méthodes d’apprentissage profond, probabilistes et non-supervisées qui utilisent l’inférence variationnelle pour créer des modèles évolutifs de grande capacité pour ce type de données. Nous présentons deux classes d’apprentissage variationnel profond, puis nous les appliquons à deux problèmes spécifiques liés au domaine maritime. La première application est l’identification de systèmes dynamiques à partir de données bruitées et partiellement observées. Nous introduisons un cadre qui fusionne l’assimilation de données classique et l’apprentissage profond moderne pour retrouver les équations différentielles qui contrôlent la dynamique du système. En utilisant une formulation d’espace d’états, le cadre proposé intègre des composantes stochastiques pour tenir compte des variabilités stochastiques, des erreurs de modèle et des incertitudes de reconstruction. La deuxième application est la surveillance du trafic maritime à l’aide des données AIS. Nous proposons une architecture d’apprentissage profond probabiliste multitâche pouvant atteindre des performances très prometteuses dans différentes tâches liées à la surveillance du trafic maritime, telles que la reconstruction de trajectoire, l’identification du type de navire et la détection d’anomalie, tout en réduisant considérablement la quantité de données à stocker et le temps de calcul. temps. Pour la tâche la plus importante - la détection d’anomalie, nous introduisons un détecteur géospatialisé qui utilise l’apprentissage profond variationnel pour construire une représentation probabiliste des trajectoires AIS, puis détecter les anomalies en jugeant la probabilité de cette trajectoire<br>This thesis work focuses on a class of unsupervised, probabilistic deep learning methods that use variational inference to create high capacity, scalable models for time series modelling and analysis. We present two classes of variational deep learning, then apply them to two specific problems related to the maritime domain. The first application is the identification of dynamical systems from noisy and partially observed data. We introduce a framework that merges classical data assimilation and modern deep learning to retrieve the differential equations that control the dynamics of the system. Using a state space formulation, the proposed framework embeds stochastic components to account for stochastic variabilities, model errors and reconstruction uncertainties. The second application is maritime traffic surveillance using AIS data. We propose a multitask probabilistic deep learning architecture can achieve state-of-the-art performance in different maritime traffic surveillance related tasks, such as trajectory reconstruction, vessel type identification and anomaly detection, while reducing significantly the amount data to be stored and the calculation time. For the most important task—anomaly detection, we introduce a geospatial detector that uses variational deep learning to builds a probabilistic representation of AIS trajectories, then detect anomalies by judging how likely this trajectory is
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Books on the topic "Traffic surveillance system"

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SC-186, RTCA (Firm). Minimum aviation system performance standards for aircraft surveillance applications (ASA). RTCA, Inc., 2003.

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Benoît, André. Efficient conduct of individual flights and air traffic or Optimum utilization of modern technology (guidance, control, navigation, communication, surveillance and processing facilities) for the overall benefit of civil and military airspace users. AGARD, 1986.

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G, Cooper Eric, Society of Automotive Engineers, and United States. National Aeronautics and Space Administration., eds. Support of helicopter "free-flight" operations in the 1996 olympics: 1996 World Aviation Congress, October 21-24, 1996, Los Angeles, CA. SAE International, 1997.

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G, Cooper Eric, Society of Automotive Engineers, and United States. National Aeronautics and Space Administration., eds. Support of helicopter "free-flight" operations in the 1996 olympics: 1996 World Aviation Congress, October 21-24, 1996, Los Angeles, CA. SAE International, 1997.

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(Firm), Rockwell Collins, and Langley Research Center, eds. Runway incursion prevention system ADS-B and DGPS data link analysis, Dallas-Ft. Worth International Airport. National Aeronautics and Space Administration, Langley Research Center, 2001.

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Office, General Accounting. Air traffic control: Voice communications system continues to encounter difficulties : report to congressional requesters. The Office, 1989.

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Office, General Accounting. Air traffic control: FAA's advanced automation system acquisition strategy is risky : report to the Secretary of Transportation. The Office, 1986.

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Office, General Accounting. Air traffic control: Efforts to expand the New York terminal area automation system : report to the chairman, Subcommittee on Transportation and Related Agencies, Committee on Appropriations, U.S. Senate. The Office, 1988.

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Kudryakov, Sergey, Valeriy Kul'chickiy, Nikolay Povarenkin, Viktor Ponomarev, Evgeniy Rubcov, and Evgeniy Sobolev. Radio engineering support of aircraft flights and aviation telecommunications. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1242223.

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The training manual describes the basics of radio engineering support for flights, the organization of radio engineering support for flights, and the general characteristics of flight support equipment. Information is provided about drive radios, marker beacons, radio beacon landing systems, automatic direction finders, RSBN system, VOR and DME beacons, satellite navigation systems, as well as radar surveillance equipment. The basics of telecommunications, issues of aviation telecommunications, as well as information about the means of aviation telecommunications are presented. There are questions for self-control. &#x0D; It is intended for students studying under the specialty program in the specialty 25.05.05 "Aircraft operation and air traffic management"; for students studying under the bachelor's program in the direction of training 25.03.04 "Airport operation and aircraft flight support", as well as for students studying under the master's program in the direction 25.04.04 "Airport Operation and aircraft flight support".
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Geyer, Michael. Surveillance alternatives: Cost estimates and technical considerations for the en route domain. Federal Aviation Administration, Office of Surveillance, 1998.

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Book chapters on the topic "Traffic surveillance system"

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Toal, A. F., and H. Buxton. "Spatio-temporal reasoning within a traffic surveillance system." In Computer Vision — ECCV'92. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_103.

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Filonenko, Alexander, Andrey Vavilin, Taeho Kim, and Kang-Hyun Jo. "Augmented Reality Surveillance System for Road Traffic Monitoring." In Intelligent Computing Methodologies. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09339-0_32.

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Sun, Pengfei, and Qinghe Liu. "Intelligent Traffic Accident Detection System Using Surveillance Video." In Proceedings of China SAE Congress 2020: Selected Papers. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2090-4_61.

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Ukani, Vijay, Sanjay Garg, Chirag Patel, and Hetali Tank. "Efficient Vehicle Detection and Classification for Traffic Surveillance System." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5427-3_51.

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Yu, Shih-Hao, Jun-Wei Hsieh, Yung-Sheng Chen, and Wen-Fong Hu. "An Automatic Traffic Surveillance System for Vehicle Tracking and Classification." In Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_52.

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Ilakkiya, S., R. Abinaya, R. Shalini, K. Kiruthika, and C. Jackulin. "Surveillance System for Golden Hour Rescue in Road Traffic Accidents." In Emerging Trends in Computing and Expert Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32150-5_132.

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Palepu, Ramesh Babu, Shaik Asleem, and K. Lakshmi Padmavathi. "Accident Analysis and Detection System Based on Traffic Surveillance Videos." In Disruptive technologies in Computing and Communication Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781032665535-31.

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Navarrete, Mauricio, and Paulo Cugnasca. "Automatic Dependent Surveillance in the Air Traffic System — a Probabilistic Approach." In Probabilistic Safety Assessment and Management. Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_80.

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Phan, Hung Ngoc, Long Hoang Pham, Duong Nguyen-Ngoc Tran, and Synh Viet-Uyen Ha. "Occlusion Vehicle Segmentation Algorithm in Crowded Scene for Traffic Surveillance System." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7512-4_58.

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Kamakula, Kamaraju, J. Sharmila Rani, G. Santhosi, and G. Gowri Pushpa. "Tracking and Speed Estimation of Moving Vehicle for Traffic Surveillance System." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2526-3_70.

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Conference papers on the topic "Traffic surveillance system"

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Kim, Taek-Lim, Byung Jin Jang, Joo Yeon Yeon, Tae-Hyeong Kim, and Tae-Hyoung Park. "Camera-LiDAR Jaywalking Detection in Traffic Surveillance System." In 2025 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2025. https://doi.org/10.1109/sii59315.2025.10871006.

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Cai, Guoci, Lihan Tong, Jie Pan, Ming Chen, Zhengxuan Huang, and Yiliang Wu. "Traffic Surveillance License Plate Recognition System in Heavy Rainfall." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653221.

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Caruso, A., L. Galluccio, C. Grasso, et al. "Advancing Urban Traffic Monitoring in Smart Cities: A Field Experiment with UAV-Based System for Transport Planning and Intelligent Traffic Management." In 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2025. https://doi.org/10.1109/icns65417.2025.10976747.

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Özdemir, Mustafa, Öznur Usanmaz, Philippe Bouchaudon, and Nicolas Gorgues. "A Decision Support System for Conflict Resolution: Interfacing a Genetic Algorithm with an Air Traffic Control Simulator." In 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2025. https://doi.org/10.1109/icns65417.2025.10976956.

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Bloisi, Domenico, Luca Iocchi, Daniele Nardi, Michele Fiorini, and Giovanni Graziano. "Ground traffic surveillance system for Air Traffic control." In 2012 12th International Conference on ITS Telecommunications (ITST). IEEE, 2012. http://dx.doi.org/10.1109/itst.2012.6425151.

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Kaplan, Mehmet, and Muhittin Gokmen. "Automated and accurate traffic surveillance system." In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5653566.

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Allgood, Glenn O., Regina K. Ferrell, Stephen W. Kercel, and Ruth A. Abston. "Traffic flow wide-area surveillance system." In Photonics for Industrial Applications, edited by Richard J. Becherer. SPIE, 1995. http://dx.doi.org/10.1117/12.198915.

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Yeddanapudi, Murali, Yaakov Bar-Shalom, Krishna R. Pattipati, and Richard R. Gassner. "MATSurv: multisensor air traffic surveillance system." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Oliver E. Drummond. SPIE, 1995. http://dx.doi.org/10.1117/12.217710.

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Schwartz, William C., and Robert A. Olson. "Wide-area traffic surveillance (WATS) system." In Photonics East '96, edited by Alan C. Chachich and Marten J. de Vries. SPIE, 1997. http://dx.doi.org/10.1117/12.267138.

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Desai, Yash, Yashowardhan Rungta, and Parth Reshamwala. "Automatic Traffic Management and Surveillance System." In 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). IEEE, 2020. http://dx.doi.org/10.1109/icsidempc49020.2020.9299578.

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Reports on the topic "Traffic surveillance system"

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Allgood, G. O., R. K. Ferrell, S. W. Kercel, R. A. Abston, C. L. Carnal, and P. I. Moynihan. Traffic flow wide-area surveillance system definition. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/28302.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I. Purdue University, 1990. http://dx.doi.org/10.5703/1288284314184.

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Wang, Mu-Han, and Michael Cassidy. An Electronic Surveillance and Control System for the Management of Traffic on the Borman Expressway. Purdue University, 1996. http://dx.doi.org/10.5703/1288284313138.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I. Purdue University Press, 1990. http://dx.doi.org/10.5703/1288284313426.

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Cassidy, Michael, and Kumares Sinha. An Electronic Surveillance and Control System for Traffic Management on the Borman Expressway, Part I : Executive Summary. Purdue University, 1990. http://dx.doi.org/10.5703/1288284314185.

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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Abstract:
In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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