Relevant bibliographies by topics / Traffic system control

Contents

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

Academic literature on the topic 'Traffic system control'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Traffic system control"

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Gaikwad, Anand, Shreya Shreya, and Shivani Patil. "Vehicle Density Based Traffic Control System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 511–14. http://dx.doi.org/10.31142/ijtsrd10938.

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suryawanshi, Prof Ranjeetsingh, Ms Pooja Mahajan, and Ms Harshada Jagtap. "Traffic Control System." IJARCCE 7, no. 11 (November 30, 2018): 183–85. http://dx.doi.org/10.17148/ijarcce.2018.71141.

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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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Zaidi, Subiya, Harshita Yadav, Hemant Kumar Chaudhary, Hrithik Puri, and Kartikeya Saraswat. "Dynamic Traffic Control System." Journal of Big Data Technology and Business Analytics 1, no. 2 (July 28, 2022): 25–31. http://dx.doi.org/10.46610/jbdtba.2022.v01i02.004.

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A country with a population of 1.3 billion and almost 300 million vehicles, India is one of the biggest contributors to traffic jams, vehicle- specific pollution, and chronic lung diseases. To manage the footfall of this gigantic urban population, and the vehicles, our country has blindly poured in resources towards both active and passive traffic management, investing in measures such as smart traffic management systems and deploying enormous armies of traffic police to handle intersections and exits. The paper aims, towards showcasing a dynamic, fully autonomous model, that uses real-time feeds from existing traffic junctions/ intersection cameras, process them and provide an intensity score based on the density of traffic in each adjoining lane. The system, based upon the intensity scores, provides suitable traffic go time to each lane. The model also scans for emergency vehicles in each lane, to provide a priority pass to such vehicles.
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Galkin, A. V., A. S. Sysoev, and N. S. Bondar. "AUTOMATED TRAFFIC CONTROL SYSTEM." Mathematical Methods in Technologies and Technics, no. 7 (2022): 11–15. http://dx.doi.org/10.52348/2712-8873_mmtt_2022_7_11.

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Pardesi, M. A., Saquib S. Aowte, Tanmay V. Kakare, Revati U. Bhosale, and Swayambhoo R. Pati. "ADAPTIVE TRAFFIC CONTROL SYSTEM." International Journal of Engineering Applied Sciences and Technology 04, no. 09 (January 30, 2020): 421–23. http://dx.doi.org/10.33564/ijeast.2020.v04i09.056.

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Chandel, Shiva, Shubhransh Yadav, and Sandeep Yadav. "Modern traffic control system." Malaya Journal of Matematik S, no. 1 (January 2018): 22–25. http://dx.doi.org/10.26637/mjm0s01/04.

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R U, Yawle, Kiran K.Modak, Parmeshwar S.Shivshette, and Snehal S.Vhaval. "Smart Traffic Control System." International Journal of Electronics and Communication Engineering 3, no. 3 (March 25, 2016): 20–23. http://dx.doi.org/10.14445/23488549/ijece-v3i3p106.

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Choudhary, Vineeta, Shrey Tank, Kriti Gulati, Shubham Jani, and Bhaumik Machhi. "Adaptive Traffic Control System." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 495–99. http://dx.doi.org/10.22214/ijraset.2023.49068.

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Abstract: Whenever we hear the word traffic, we all think of people blaring horns and waiting endlessly, stress is always silent but prominent in the word traffic. We all face traffic daily and have experienced the frustration and stress that traffic induces. Our project focuses on reducing the traffic waiting time significantly and hence reducing air and noise pollution. Less waiting time at traffic means less wastage of fuel leading to energy conservation in the times of a global warming
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Gopalakrishnan, Karthik, and Hamsa Balakrishnan. "Control and Optimization of Air Traffic Networks." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 397–424. http://dx.doi.org/10.1146/annurev-control-070720-080844.

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The air transportation system connects the world through the transport of goods and people. However, operational inefficiencies such as flight delays and cancellations are prevalent, resulting in economic and environmental impacts. In the first part of this article, we review recent advances in using network analysis techniques to model the interdependencies observed in the air transportation system and to understand the role of airports in connecting populations, serving air traffic demand, and spreading delays. In the second part, we present some of our recent work on using operational data to build dynamical system models of air traffic delay networks. We show that Markov jump linear system models capture many of the salient characteristics of these networked systems. We illustrate how these models can be validated and then used to analyze system properties such as stability and to design optimal control strategies that limit the propagation of disruptions in air traffic networks.
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Dissertations / Theses on the topic "Traffic system control"

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Yan, Li. "On the traffic flow control system." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39431174.

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Yan, Li, and 顏理. "On the traffic flow control system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39431174.

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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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Negi, Pallav. "Artificial Immune System based urban traffic control." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5764.

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Borrowing ideas from natural immunity, Artificial Immune Systems (AIS) offer a novel approach to solving many diagnosis, optimization and control problems. In the course of this research this paradigm was applied to the problem of optimizing urban traffic. The traffic was micro-simulated with each car on a two junction road system modeled individually. The cars themselves were programmed with 'personalities' to better simulate real traffic. A novel AIS was developed to detect, predict, and control anomalous traffic conditions. It was also used to optimize the flow of traffic through the road network. Benchmarking was performed against the well accepted TRANSYT traffic control system. Though the TRANSYT system performed better initially, the AIS control showed marked improvement over time as it adapted better to changing traffic conditions. This change was expected as TRANSYT is optimized for specific initial conditions unlike the AIS system which adapts to changes.
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Lin, Joyce C. (Joyce Chaisin) 1979. "VisualFlight : the air traffic control data analysis system." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87266.

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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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Chow, Andy Ho Fai. "Adaptive traffic control system : a study of strategies, computational speed and effect of prediction error /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202002%20CHOW.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 126-129). Also available in electronic version. Access restricted to campus users.
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Grau, Mariani Rafael. "A demand-responsive traffic control system for urban areas." Doctoral thesis, Universitat Politècnica de Catalunya, 1995. http://hdl.handle.net/10803/399670.

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The goal of this Ph.D. Thesis is the design, development and testing of a 'well-engineered system' aimed at the demand-responsive traffic control of urban areas. Because of the author's background -software engineering-, a 'well-engineered system' means a system that is both efficient at performing its task in a wide range of conditions and also that has been built from the robust design's and ease-of-use's points of view. The work has proceed in four steps and, accordingly, this document has been divided in four parts that are briefly desc1ibed here. PART I is, mainly, an introduction to the subject. It introduces the concept of demand­ responsiveness in traffic control, providing a historical perspective from traditional fixed­ control systems to current modern demand-responsive systems. The part concludes with a study of areas where research and improvement is needed. All of these areas are considered in the following parts, where some solutions are proposed. PAR T II presents the design, development and preliminary tests of a demand­ responsive traffic control system, CARS V l, intended to operate in signalized urban areas: networks, arte1ials, and isolated intersections. A graphical user interface, X-Windows and DecWindows compliant, allows the user to specify network characteristics in a friendly and intuitive manner, without the need to be acquainted with the actual modeling. The system features an underlying simulation system and a prediction model based on real-time measured conditions, implements a centralized approach based on small variations, and has flexible detector positioning-and-number requirements. PART III presents the design of a simulation environment, named GETRAM, that solves the difficulties in testing CARS that were discussed at the end of the previous part. We have seen that these difficulties are inherent in the traffic engineer having to use diverse models in order to analyze a traffic network, and that there is the need for a system to salve it. GETRAM provides a unified framework integrating various types of traffic models and tools for traffic analysis, sharing a DataBase, a graphical editor and a mod le for results presentation. The traffic network can be partitioned into views, hierarchically organized polygons in the real world, so that, for example, a simulation model applies only to one of these restricted areas. Network statcs produced by one model can be used as a starting point by another modcl. I n order to ease the task of integrating a new model or analysis tool, a library of object-based high-level functions provide a view-aware access to the DataBase, maintaining consistency. Included in this Ph.D. thesis are the design of the whole environment -DataBase, G ETR AM API, and graphical editor - and the development of the DataBase and GETRAM API. PART IV starts from the demand-responsive traffic control system developed in PART II, CARS V l , and improves it in various respects. First, in order to ease the task of testing the system, it is integrated into the traffic modeling and analysis environment described in PART III. Second, certain parts that directly influence to the effectiveness of the system, such as control timing, adaptive control logics and communication with the controllers, are revised or totally redone. A suite of tests has been applied to the resulting system, CARS V2, in the four scenarios desc1ibed in PART 11. Finally, to further testing the system and taking advantage of the fact of having real-world data available, it is compared against a vehicle-actuated control at an isolated junction.
El objetivo de esta tesis es el diseño, desarrollo y test por simulación microscópica de un sistema autoadaptativo apto para cruces aislados, arterias y redes urbanas complejas. El sistema produce planes de control acíclicos y presenta unos requerimientos de tiempo real muy flexibles debidos a utilizar una nueva secuencia cíclica de tareas en la que se predice el estado del sistema a corto término antes de probar planes de control alternativos. Estas pruebas se realizan mediante un modelo interno de simulación que sigue un enfoque mesoscópico a base de paquetes de vehículos de velocidad variable, con el que se consigue modelizar la dinámica de colas de vehículos de forma más exacta que con los sistemas actualmente existentes. Esto da una ventaja, corroborada en los test, en condiciones de flujo altas, con lo que el sistema de controles es capaz de mantener una buena efectividad en un amplio rango de condiciones de tráfico. El sistema viene acompañado de un entorno de simulación y test que aporta un alto grado de integración y de facilidad de uso, a lo largo de todo el proceso de especificación de geometría, parámetros y ejecución de simulación se mantiene una vista de la red de tráfico altamente realista.
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Abdelfatah, Akmal Saad. "Time-dependent signal control and system optimal traffic assignment in congested vehicular traffic networks /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Yu, Tungsheng. "On-line Traffic Signalization using Robust Feedback Control." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/26334.

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The traffic signal affects the life of virtually everyone every day. The effectiveness of signal systems can reduce the incidence
of delays, stops, fuel consumption, emission of pollutants, and accidents. The problems related to rapid growth in traffic
congestion call for more effective traffic signalization using robust feedback control methodology.

Online traffic-responsive signalization is based on real-time traffic conditions and selects cycle, split, phase, and offset for the intersection according to detector data. A robust traffic feedback control begins with assembling traffic demands, traffic facility supply, and feedback
control law for the existing traffic operating environment. This information serves the input to the traffic control process
which in turn provides an output in terms of the desired performance under varying conditions.

Traffic signalization belongs to a class of hybrid systems since the differential equations model the continuous behavior of the traffic flow dynamics and finite-state machines model the discrete state changes of the controller. A complicating aspect, due to the state-space
constraint that queue lengths are necessarily nonnegative, is that the continuous-time system dynamics is actually the
projection of a smooth system of ordinary differential equations. This also leads to discontinuities in the boundary dynamics
of a sort common in queueing problems.
The project is concerned with the design of a feedback controller to minimize accumulated queue lengths in the presence of unknown inflow disturbances at an isolated intersection and a traffic network with some signalized intersections. A dynamical system has finite L2-gain if it is dissipative in some sense. Therefore, the Hinfinity-control problem turns to designing a controller such that the resulting closed loop system is dissipative, and correspondingly there exists a storage function.

The major contributions of this thesis include 1) to propose state space models for both isolated multi-phase intersections and a class of queueing networks; 2) to formulate Hinfinity problems for the control systems with persistent disturbances; 3) to present the projection dynamics aspects of the problem to account for the constraints on the state variables; 4) formally to study this problem as a hybrid system; 5) to derive traffic-actuated feedback control laws for the multi-phase intersections. Though we have mathematically presented a robust feedback solution for the traffic signalization, there still remains some distance before the physical implementation. A robust adaptive control is an interesting research area for the future traffic signalization.
Ph. D.

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Books on the topic "Traffic system control"

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FEDERAL AVIATION ADMINISTRATION. Traffic management system. [Washington, D.C.?]: U.S.Dept. of Transportation, Federal Aviation Administration, 1985.

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Brenlove, Milovan S. The air traffic system: A commonsense guide. Ames: Iowa State University Press, 1987.

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Agent, Kenneth R. Evaluation of the ADAPTIR system for work zone traffic control. Lexington, KY: Kentucky Transportation Center, College of Engineering, University of Kentucky, 1999.

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Heister, H. Dale. The air traffic control system: Past and present facilities. Santa Barbara, Calif: H.D. Heister, 1990.

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Wright, Jamie N. Air traffic safety and control issues. Edited by Air Traffic Organization (U.S.). Hauppauge, N.Y: Nova Science Publishers, 2011.

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He, H. Object oriented domain model for air traffic control system. Manchester: UMIST, 1997.

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FEDERAL AVIATION ADMINISTRATION. Project implementation plan long-range: Radar system relocation project (ARSR-3 Leapfrog). [Washington, D.C.?]: U.S. Dept. of Transportation, Federal Aviation Administration, 1993.

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Green, Steven M. The Center/TRACON automation system (CTAS): A video presentation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1992.

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RTCA Free Flight Steering Committee. National airspace system: Concept of operations and vision for the future of aviation. Washington, DC: RTCA, 2002.

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

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Book chapters on the topic "Traffic system control"

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Kalić, Milica, Slavica Dožić, and Danica Babić. "Air Traffic Control System." In Introduction to the Air Transport System, 118–54. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003102533-4.

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Darshane, Shriniwas V., Ranjeet B. Kagade, and Somnath B.Thigale. "Involuntary Traffic Control System." In Techno-Societal 2020, 209–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69921-5_22.

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Adwani, Kakan, and N. Rakesh. "Smart City Traffic Control System." In Intelligent Communication Technologies and Virtual Mobile Networks, 421–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28364-3_42.

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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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Czarczyński, Wcjciech, Ryszard Klempous, and Jan Nikodem. "Multilevel approach to traffic control system." In Lecture Notes in Computer Science, 561–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0025075.

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Zhao, Jianyu, Diankui Tang, Xin Geng, and Lei Jia. "Urban Arterial Traffic Coordination Control System." In Artificial Intelligence and Computational Intelligence, 275–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16527-6_35.

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Sathiyaraj, R., A. Bharathi, and B. Balamurugan. "Intelligent Traffic Light Control and Ambulance Control System." In Advanced Intelligent Predictive Models for Urban Transportation, 99–118. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003217367-7.

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Młyńczak, Jakub, Andrzej Toruń, and Lucyna Bester. "European Rail Traffic Management System (ERTMS)." In Studies in Systems, Decision and Control, 217–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19150-8_7.

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Logsdon, Tom. "Digital Avionics and Air Traffic Control." In The Navstar Global Positioning System, 165–76. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3104-3_12.

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Bestugin A.R., Eshenko A.A., Filin A.D., Plyasovskikh A.P., Shatrakov A.Y., and Shatrakov Y.G. "Technical Requirements to the ATC Automation System Simulators for Controllers." In Air Traffic Control Automated Systems, 145–53. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9386-0_4.

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Conference papers on the topic "Traffic system control"

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Hahanov, V. I., O. A. Gus, A. Ziarmand, Ngene Christopher Umerah, and A. Arefjev. "Cloud traffic control system." In 2013 11th East-West Design and Test Symposium (EWDTS). IEEE, 2013. http://dx.doi.org/10.1109/ewdts.2013.6673092.

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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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Dubey, Kajal, and Tushar Gupta. "Adaptive Traffic Control System: The Smart And Imperative Traffic Control System For India." In 2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2020. http://dx.doi.org/10.1109/iciem48762.2020.9160175.

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Boudaakat, Sidina, Ahmed Rebbani, and Omar Bouattane. "Smart Traffic Control System for Decreasing Traffic Congestion." In 2019 Fourth International Conference on Systems of Collaboration Big Data, Internet of Things & Security ( SysCoBIoTS). IEEE, 2019. http://dx.doi.org/10.1109/syscobiots48768.2019.9028014.

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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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Diaz, Nicole, Jorge Guerra, and Juan Nicola. "Smart Traffic Light Control System." In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM). IEEE, 2018. http://dx.doi.org/10.1109/etcm.2018.8580282.

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Zongyao, Wang, and Sui Cong. "Distributed traffic network control system." In 2013 IEEE International Conference on Signal Processing, Communications and Computing. IEEE, 2013. http://dx.doi.org/10.1109/icspcc.2013.6663906.

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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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Ghazal, Bilal, Khaled ElKhatib, Khaled Chahine, and Mohamad Kherfan. "Smart traffic light control system." In 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA). IEEE, 2016. http://dx.doi.org/10.1109/eecea.2016.7470780.

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Shinde, Swapnil Manohar. "Adaptive traffic light control system." In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM). IEEE, 2017. http://dx.doi.org/10.1109/icisim.2017.8122189.

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Reports on the topic "Traffic system control"

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Demael, Jacques J., and Alexander H. Levis. On the Generation of a Variable Structure Airport Surface Traffic Control System. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada211306.

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H H AEROSPACE DESIGN CO INC BEDFORD MA. Advanced Air Traffic Control Concept Study: Automated Tactical Aircraft Launch and Recovery System (ATALARS). Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada229100.

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

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7

Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and converted from the research code to its implementational version written in the C++ programming language. Two trailer-based TScan units have been built. The design of the prototype was iterated multiple times to account for component placement, ease of maintenance, etc. The expansion of the TScan system from a one single-sensor unit to multiple units with multiple LiDAR sensors necessitated transforming all the measurements into a common spatial and temporal reference frame. Engineering applications for performing traffic counts, analyzing speeds at intersections, and visualizing pedestrian presence data were developed. The limitations of the existing SSAM for traffic conflicts analysis with computer simulation prompted the research team to develop and implement their own traffic conflicts detection and analysis technique that is applicable to real-world data. Efficient use of the development system requires proper training of its end users. An INDOT-CRS collaborative process was developed and its execution planned to gradually transfer the two TScan prototypes to INDOT’s full control. This period will be also an opportunity for collecting feedback from the end user and making limited modifications to the system and documentation as needed.
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Li, Howell, Enrique Saldivar-Carranza, Jijo K. Mathew, Woosung Kim, Jairaj Desai, Timothy Wells, and Darcy M. Bullock. Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317212.

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Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds.
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Asaturov, M. U., and G. K. Korendyasev. ENHANCEMENT OF VIBRATION STABILITY OF TERMINAL EQUIPMENT CASES OF VOICE COMMUNICATION CONTROL SYSTEMS (VCCS) FOR AIR TRAFFIC CONTROLLERS. DOI СODE, 2021. http://dx.doi.org/10.18411/vntr2021-163-1.

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David, Aharon. Unsettled Topics Concerning Airport Cybersecurity Standards and Regulation. SAE International, September 2021. http://dx.doi.org/10.4271/epr2021020.

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A large international airport is a microcosm of the entire aviation sector, hosting hundreds of different types of aviation and non-aviation stakeholders: aircraft, passengers, airlines, travel agencies, air traffic management and control, retails shops, runway systems, building management, ground transportation, and much more. Their associated information technology and cyber physical systems—along with an exponentially resultant number of interconnections—present a massive cybersecurity challenge. Unlike the physical security challenge, which was treated in earnest throughout the last decades, cyber-attacks on airports keep coming, but most airport lack essential means to confront such cyber-attacks. These missing means are not technical tools, but rather holistic regulatory directives, technical and process standards, guides, and best practices for airports cybersecurity—even airport cybersecurity concepts and basic definitions are missing in certain cases. Unsettled Topics Concerning Airport Cybersecurity Standards and Regulation offers a deeper analysis of these issues and their causes, focusing on the unique characteristics of airports in general, specific cybersecurity challenges, missing definitions, and conceptual infrastructure for the standardization and regulation of airports cybersecurity. This last item includes the gaps and challenges in the existing guides, best-practices, standards, and regulation pertaining to airport cybersecurity. Finally, practical solution-seeking processes are proposed, as well as some specific potential frameworks and solutions.
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