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1

Kara, T., and Savas M. Cengiz. "Design and Simulation of a Decentralized Railway Traffic Control System." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 945–51. https://doi.org/10.5281/zenodo.49885.

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With the increasing use of railway transportation, various methods have been developed for the control and management of train traffic. Train traffic control systems that are currently in use are overwhelmingly centralized systems. In this study, the development of the general structure of railway traffic control techniques is examined, centralized and decentralized control systems are investigated, and an alternative train traffic control system, the Decentralized Train Traffic Management System (DTMS), is suggested. Simulation results on the possible application of the proposed method to a railway line in South-East Turkey are employed to evaluate the performance of the developed system.  
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Kara, T., and M. Cengiz Savas. "Design and Simulation of a Decentralized Railway Traffic Control System." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 945–51. http://dx.doi.org/10.48084/etasr.631.

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With the increasing use of railway transportation, various methods have been developed for the control and management of train traffic. Train traffic control systems that are currently in use are overwhelmingly centralized systems. In this study, the development of the general structure of railway traffic control techniques is examined, centralized and decentralized control systems are investigated, and an alternative train traffic control system, the Decentralized Train Traffic Management System (DTMS), is suggested. Simulation results on the possible application of the proposed method to a railway line in South-East Turkey are employed to evaluate the performance of the developed system.
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Jacyna, Marianna, Andrzej Toruń, and Dobromir Jasiński. "Comparative analysis of the functionality of centralized and distributed LEU." Journal of Automation, Electronics and Electrical Engineering 4, no. 2 (2022): 13–16. http://dx.doi.org/10.24136/jaeee.2022.007.

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This article addresses the centralized LEU (Lineside Electronic Unit) encoder used in ERTMS/ETCS Level 1 (European Rail Traffic Management System / European Train Control System). It is explained what a centralized LEU encoder is and why this solution offers more possibilities than a distributed approach. Also explained the general principle of the encoder and how it works with switchable Eurobalise. The paper also juxtaposes how it works with and connects to the interlocking system in both the distributed and centralized approaches. The differences between the centralized and decentralized LEU encoder in diagnostic and management capabilities are also described.
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Alqubaysi, Tariq, Abdullah Faiz Al Asmari, Fayez Alanazi, Ahmed Almutairi, and Ammar Armghan. "Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles." Sensors 25, no. 4 (2025): 1116. https://doi.org/10.3390/s25041116.

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Intelligent Transport Systems (ITSs) are essential for secure and privacy-preserving communications in Autonomous Vehicles (AVs) and enhance facilities like connectivity and roadside assistance. Earlier research models used for traffic management compromised user privacy and exposed sensitive data to potential adversaries while handling real-time data from numerous vehicles. This research introduces a Federated Learning-based Predictive Traffic Management (FLPTM) system designed to optimize service access and privacy for Autonomous Vehicles (AVs) within an ITS. Moreover, a CPPS will provide strong security to mitigate adversarial threats through state modelling and authenticated access permissions for the integrity of vehicle communication networks from man-in-the-middle attacks. The suggested FLPTM system utilizes a Contained Privacy-Preserving Scheme (CPPS) that decentralizes data processing and allows vehicles to train local models without sharing raw data. The CPPS framework combines a classifier-based learning technique with state modelling and access permissions to protect user data against invasions and man-in-the-middle attacks. The proposed model leverages Federated Learning (FL) to enhance data security in collaborative machine learning processes by allowing updates that preserve privacy, enabling joint learning without exposing raw data. It addresses key challenges such as high communication costs, the impact of adversarial attacks, and access time inefficiencies. Using FL, the model reduces communication costs by 23.29%, mitigates adversarial effects by 16.1%, and improves access time by 18.95%, achieving significant cost savings and maintaining data privacy throughout the learning process.
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Sultana, Kazi Sharmin, Maksuda Begum, Joynal Abed, et al. "Blockchain-Based Green Edge Computing: Optimizing Energy Efficiency with Decentralized AI Frameworks." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 386–408. https://doi.org/10.32996/jcsts.2025.7.1.29.

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The deployment of Internet of Things (IoT) devices and edge computing has grown exponentially and has reinvented the world of data processing and making it possible to deliver low-latency applications in real-time settings. Notwithstanding, with this shift towards the use of distributed systems, we are faced with new challenges of ensuring there is effective management of energy consumption. The main aim of the proposed study was to design, deploy, and test a new decentralized edge computing framework that combines blockchain technology and artificial intelligence to achieve optimized energy efficiency. To be more precise, we intended to create AI models that are able to recognize and forecast energy usage patterns at the edge in real-time. The system of 250 edge devices on a network in this study simulated the environment of the smart infrastructure, which portrays a medium-sized U.S. urban grid. All of these devices were able to record important performance and system data on an ongoing basis over more than 30 days at a resolution of 10 seconds, and provide more than 60 million data points. Prominent variables that are recorded are CPU usage (%)/memory load (MB) and energy level (Watts), which is a reflection of the device in terms of operation strain and efficiency. So that edge workloads can be classified according to their energy consumption rates and usage trends to facilitate energy-efficient scheduling. Three supervised machine learning models were chosen: Logistic Regression, Random Forest Classifier, and Support Vector Classifier (SVC). The preprocessed dataset was divided into 80:20 train and test sets to ensure that there was no data leakage, and all three models were trained on the datasets and evaluated on the test set. Based on the measurement, Random Forest had the most accurate predictions, meaning that it tended to slightly outdo the other models in this comparison. The next two models, notably logistic Regression and SVM, respectively, had the lowest accuracy of the three models. The encountered blockchain mechanism, i.e., lightweight transaction ledgers including Hyperledger Sawtooth, offered informative transparency and traceability of energy behavior in edge networks. Introducing blockchain-based green edge computing is about to change the energy management approach in smart cities and intelligent energy grids in the U.S. The introduction of IoT-powered networks in metropolitan areas such as New York City, San Francisco, and Chicago, including traffic sensors and adaptive lighting, autonomous transportation, and Wi-Fi hotspots, has also meant that the energy requirements of distributed edge networks are being placed at a serious burden. Green edge computing with blockchain has an important role in defense and the safety of the population by assuring safe, energy-saving decision-making in the field. The DOD (U.S Department of Defense) mainly depends on mobile and distributed sensor networks to perform surveillance of the theaters of operation, environmental tracking, and real-time information. The findings of the current research add value to the potential of AI-powered methods in increasing energy efficiency in edge computing solutions, especially when combined with blockchain frameworks.
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Baranov, L. A., V. G. Sidorenko, E. P. Balakina, and L. N. Loginova. "Intelligent centralized traffic management of a rapid transit system under heavy traffic." Dependability 21, no. 2 (2021): 17–23. http://dx.doi.org/10.21683/1729-2646-2021-21-2-17-23.

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Aim. In today’s major cities, increased utilization and capacity of the rapid transit systems (metro, light rail, commuter trains with stops within the city limits) – under condi[1]tions of positive traffic safety – is achieved through smart automatic train traffic management. The aim of this paper is to choose and substantiate the design principles and architecture of such system.Methods. Using systems analysis, the design principles and architecture of the system are substantiated. Genetic algorithms allow automating train traffic planning. Methods of the optimal control theory allow managing energy-efficient train movement patterns along open lines, assigning individual station-to-station running times following the principle of mini[1]mal energy consumption, developing energy-efficient target traffic schedules. Methods of the automatic control theory are used for selecting and substantiating the train traffic algorithms at various functional levels, for constructing random disturbance extrapolators that minimize the number of train stops between stations.Results. Development and substantiation of the design principles and architecture of a centralized intelligent hierarchical system for automatic rapid transit traffic management. The distribution of functions between the hierarchy levels is described, the set of subsystems is shown that implement the purpose of management, i.e., ensuring traffic safety and comfort of passengers. The criteria are defined and substantiated of management quality under compensated and non-compensated disturbances. Traffic management and target scheduling automation algorithms are examined. The application of decision algorithms is demonstrated in the context of uncertainty, use of disturbance prediction and genetic algorithms for the purpose of train traffic planning automation. The design principles of the algorithms of traffic planning and management are shown that ensure reduced traction energy consumption. The efficiency of centralized intelligent rapid transit management system is demonstrated; the fundamental role of the system in the digitalization of the transport system is noted.Conclusion. The examined design principles and operating algorithms of a centralized intelligent rapid transit management system showed the efficiency of such systems that ensured by the following: increased capacity of the rapid transit system; improved energy efficiency of train traffic planning and management; improved train traffic safety; assurance of operational traffic management during emergencies and major traffic disruptions; improved passenger comfort.
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7

H S, Lokeshwari. "Density Based Traffic Management and Ambulance Detection Using RFID." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46680.

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Abstract-Traffic congestion is a major issue in many Indian and global cities due to signal failures, poor law enforcement, and ineffective traffic management. It negatively impacts the economy, environment, and quality of life. This project proposes a smart traffic management system using the Internet of Things and decentralized algorithms to optimize traffic flow. It predicts traffic density to reduce congestion and prioritizes emergency vehicles by turning red lights green along their route. By improving traffic control and reducing delays, especially in emergencies, the system enhances safety, saves lives, and addresses the shortcomings of traditional traffic management methods.
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8

KITAHARA, FUMIO. "Technological Innovation of Information Systems Changes Railway Exploitation. Autonomous Decentralized Train Traffic Control System." Journal of the Institute of Electrical Engineers of Japan 119, no. 2 (1999): 80–82. http://dx.doi.org/10.1541/ieejjournal.119.80.

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9

Kamenický, Dušan. "TRAFFIC MANAGEMENT SYSTEM IN TERMS OF DATA EXCHANGE." Acta Polytechnica CTU Proceedings 5 (September 15, 2016): 26. http://dx.doi.org/10.14311/app.2016.5.0026.

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Prediction of train running and IT support of conflict resolution decision for an efficient use of the existing railway infrastructure is needed. To meet these requirements standardized interfaces between infrastructure managers and railway undertakings and infrastructure description are indispensable.
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10

Boltayev, Sunnatillo. "Functions of the interlocking system on mixed traffic lines." E3S Web of Conferences 515 (2024): 01012. http://dx.doi.org/10.1051/e3sconf/202451501012.

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These days, automated rail traffic control systems help to lessen the need for human intervention in train traffic management. On the other hand, the various algorithms used by interlocking systems to coordinate the movement of trains belonging to distinct categories result in longer wait times for technological procedures at the station. The possibility of timely routes for trains of multiple categories occurs by employing the method of automatic route preparation by interlocking systems in the organization of the movement of trains of different categories. In interlocking systems, automatic route preparation can be achieved by continuously monitoring train characteristics, train traffic graphs of various train classes, and the condition of equipment involved in the function’s development. Nonetheless, it is predicated on the creation of many models to arrange the specifications of various train classes and train movements inside a single schedule type. In order to prepare the routes ahead of time for the station tracks based on the algorithms for the operation of interlocking systems for various kinds of trains, the paper analyzes the reliance of the traffic composition characteristics.
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11

Efanov, D. V., and E. M. Mikhailyuta. "Reliability and Safety Management of the Transportation Process Using Systems for Continuous Monitoring of Railway Infrastructure Facilities." World of Transport and Transportation 21, no. 2 (2023): 84–94. http://dx.doi.org/10.30932/1992-3252-2023-21-2-10.

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While analysing the problem of increasing reliability and safety of the rail transportation process, it is shown that modern train traffic control systems do not automatically consider the events of decrease in reliability of railway infrastructure facilities; however, such a linkage is quite possible. The proposed architecture of a promising train traffic control system can be based on railway automation and remote-control (telemechanics) systems, which have a safe information interface with the means of continuous monitoring of railway infrastructure facilities.The objective of the article is to present theoretical principles of managing reliability and safety of the transportation process using «new generation» automation systems, closely integrated with technical monitoring tools. A demonstrated simplified structure of the train traffic control system has an information interface with the means of continuous monitoring of railway infrastructure facilities. The developed reliability models of the train traffic control system consider the state of railway infrastructure facilities. It is shown that it is necessary to consider the safe state of the infrastructure system in the train traffic control system. Possibilities of managing the risks of reduced reliability and safety of the transportation process are shown using stationary monitoring tools for railway infrastructure facilities. The improvement of monitoring technology and the effective use of stationary monitoring systems makes it practically possible to implement the function of managing reliability and safety of the transportation process and the entire railway complex.
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12

Kochan, Andrzej, Emilia Koper, and Paweł Wontorski. "Automatic train operation – requirements analysis." WUT Journal of Transportation Engineering 121 (June 1, 2018): 161–70. http://dx.doi.org/10.5604/01.3001.0014.4576.

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This article discusses the analysis of the process of automatic train operation. The scope of the analyzed process is not narrowed to the train itself. It covers the tasks carried out at the various operational levels of the system responsible for operating the railway traffic. The authors present the tasks in the field of railway traffic management and railway traffic control and train operation, carried respectively by the dispatcher, signalman and the train driver, which can be performed automatically, i.e. without human intervention.
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13

OCHIAI, Hajime, Tatsuya NAKAMURA, and Masashi KITAI. "1F14 Real-time Train Traffic Operation Assistance System(Operation management)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1F14–1_—_1F14–9_. http://dx.doi.org/10.1299/jsmestech.2015._1f14-1_.

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14

Toš, Zdravko, Tomislav Josip Mlinarić, and Hrvoje Haramina. "Managing Rail Traffic on Commuter Lines Based on Dynamic Timetable Application." PROMET - Traffic&Transportation 23, no. 6 (2012): 413–20. http://dx.doi.org/10.7307/ptt.v23i6.176.

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The increase of demand for transport service in rail commuter traffic stipulates higher ratio of consumed infrastructure capacity. In this method of traffic flow even minor deviations from the planned timetable can have negative influence on its stability, and this can result in major reduction of the quality of transport service. This research has defined the commuter rail traffic management system model with the application of real-time timetable rescheduling. It understands the application of the decision support system during the procedure of adjusting the timetable to the real condition in traffic in the form of genetic algorithm defined on the basis of the valid rules for the train and traffic control. Besides, this model in all the commuter trains understands the existence of the driver advisory system which is based on the algorithm for determination of the most favourable running regime with the aim of saving in energy consumption. The paper proves that by applying the proposed model the commuter rail traffic can be improved regarding the increase of the timetable stability and energy-efficient train operation. KEY WORDS: rail traffic management, genetic algorithm, energy efficient timetabling and train operation
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Keith, Alexander, Thanigajan Sangarapillai, Abdulaziz Almehmadi, and Khalil El-Khatib. "A Blockchain-Powered Traffic Management System for Unmanned Aerial Vehicles." Applied Sciences 13, no. 19 (2023): 10950. http://dx.doi.org/10.3390/app131910950.

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The increasing popularity and usage of unmanned aerial vehicles (UAVs) has brought about new challenges in airspace management. With the number of drones expected to grow even further in the coming years, there is an urgent need for an autonomous traffic management system (TMS) that can safely and effectively manage drone traffic in the airspace. It is critical that this TMS be built with principles of the Confidentiality, Integrity, and Availability (CIA) triad. In this paper, a traffic management system for UAVs is presented that takes advantage of a Hyperledger Fabric blockchain network. The TMS provides a decentralized and secure method to manage and deconflict drone flight paths, allowing for safe navigation in crowded airspaces. Through a series of simulated experiments, we demonstrated the system’s capabilities in handling path creation, multiple conflict resolutions, and large numbers of drones. Simulated tests showed that the proposed system was able to handle deconfliction of 1000 drones inside of a one square kilometer, and returned calculated paths for drones in 60 to 2000 ms with up to 100 deconflictions. The Hyperledger Fabric powered traffic management system showcased the potential to leverage permissioned blockchain technology in improving drone traffic management.
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Wolny, Włodzimierz, and Maciej Puchała. "Innovative procedures for driving an electric locomotive." Zarządzanie Innowacyjne w Gospodarce i Biznesie, no. 2/37 (March 19, 2024): 189–215. http://dx.doi.org/10.25312/2391-5129.37/2023_13wwmp.

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This article offers a historical overview of railway traffic control devices used in the past. They laid the foundations for the construction of further, improved devices that have a decisive impact on the safety of railway traffic. Shape signaling is discussed, which with minor changes is still widely used around the world. Attention is also paid to traffic lights and a comparison of traffic lights in force in Poland and the Germany is provided. The article then discusses train traffic management based on semi-automatic and automatic blocking. The work presents two safety systems of conducting rail traffic: the SHP system (Automatic Braking of a Train) and the PZB system (intermittent automatic train running control). A significant part of the work is devoted to the ETCS system being implemented throughout Europe and beyond. The principle of operation, supervision levels and operating modes of the system are characterized. The style of driving a train and its impact on energy consumption is also discussed. This leads to a consideration of whether changes in the style of driving can, in practice, bring measurable effects in the form of energy savings.
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Muhammad, Muhammad Hassan Ghulam, Reyaz Ahmad, Areej Fatima, Abdul Salam Mohammed, Muhammad Ahsan Raza, and Muhammad Adnan Khan. "Secure and transparent traffic congestion control system for smart city using a federated learning approach." International Journal of ADVANCED AND APPLIED SCIENCES 11, no. 7 (2024): 1–10. http://dx.doi.org/10.21833/ijaas.2024.07.001.

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This study addresses the increasing problems of traffic congestion in smart cities by introducing a Secure and Transparent Traffic Congestion Control System using federated learning. Traffic congestion control systems face key issues such as data privacy, security vulnerabilities, and the necessity for joint decision-making. Federated learning, a type of distributed machine learning, is effective because it allows for training models on decentralized data while maintaining data privacy. Furthermore, incorporating blockchain technology improves the system’s security, integrity, and transparency. The proposed system uses federated learning to securely gather and analyze local traffic data from different sources within a smart city without moving sensitive data away from its original location. This method minimizes the risk of data breaches and privacy issues. Blockchain technology creates a permanent, transparent record for monitoring and confirming decisions related to traffic congestion control, thereby promoting trust and accountability. The combination of federated learning's decentralized nature and blockchain's secure, transparent features aids in building a strong traffic management system for smart cities. This research contributes to advancements in smart city technology, potentially improving traffic management and urban living standards. Moreover, tests of the new combined model show a high accuracy rate of 97.78% and a low miss rate of 2.22%, surpassing previous methods. The demonstrated efficiency and adaptability of the model to various smart city environments and its scalability in expanding urban areas are crucial for validating its practical use in real-world settings.
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Modrzejewska, Aleksandra. "Automatic train operation in the CBTC system." WUT Journal of Transportation Engineering 131 (December 1, 2020): 99–118. http://dx.doi.org/10.5604/01.3001.0014.9004.

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Rail transport adapts to the requirements of the modern agglomerations and provides solutions that ensure greater speed and capacity, while being environmentally friendly. Alternative means of rail transport as well as train control systems are proposed. The basis of innovative, effective, attractive and, above all, safe railway is a traffic automation, which can be implemented to a varying range and degree. Automation of systems of the train control and railway traffic management is an area that is constantly being scientifically researched and developed. The most technologically advanced control systems, in which the human factor is eliminated, are CBTC systems. This article presents the characteristics and components of one of the CBTC class family solutions used in the world, i.e. Bombardier’s product - CITYFLO 650. On the example of the CITYFLO 650 solution, the analysis of the fully automatic train operation was performed. Each of the stages of the fully automatic train operation was included in this paper. The conducted analysis confirms the legitimacy of using the CBTC technology on very demanding city lines. Bidirectional train-to-wayside data communications and determination of train location to a high degree of precision make it that CBTC systems fulfill the criteria set by large, fast-growing cities and a growing population. Poland, as a country currently looking for solutions that would reduce the transport problems of large cities, is considering such innovative proposals for rail traffic.
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Nikitin, Aleksandr, Oleg Nasedkin, Andrey Lykov, Natal'ya Zhuravleva, Anatoliy Kornienko, and Dmitriy Kopytov. "Construction of Distributed Microprocessor-Based Train Control Systems." Transport automation research 9, no. 2 (2023): 153–61. http://dx.doi.org/10.20295/2412-9186-2023-9-02-153-161.

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High material requirements of new relay systems make it impossible to modernize devices within existing areas and require the construction of new interlocking system buildings, which significantly affects the volume of capital investments. Transitioning to microelectronic and microprocessor electronic platforms allows for the design of more compact systems with expanded functional capabilities. In global practice, there is a trend towards widespread use of microprocessor-based interlocking system. However, there is a slow pace of device updates, mainly due to the high cost of microprocessor-based interlocking system, which exceeds by several times the cost of similar relay-based interlocking system. The high performance of computing resources and fiber-optic data transmission channels allows for the design of efficient structures for microprocessor-based interlocking system. The article presents a comparative analysis of the structures of microprocessor-based interlocking system. The implemented decentralized structures solve the issues of reducing investments for the design and construction of the station’s cable network. The advantages of using these systems from the point of view of controlling the transportation process are described, conclusions are drawn on the effective use of the capabilities of modern control systems with integrated subsystems in relation to field-based train traffic control technologies.
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Dyduch, Janusz, and Mieczysław Kornaszewski. "New systems in management of railway traffic in Poland." Transportation Overview - Przeglad Komunikacyjny 2017, no. 10 (2017): 45–53. http://dx.doi.org/10.35117/a_eng_17_10_06.

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The new computer's solutions and microprocessor technology, microcomputers and the programmable controllers (PLC) for management of train traffic, which are implemented, contribute to the creation of modern rail traffic control systems. These systems provide high reliability, low power consumption, stability and safety of the trains' movement. One of the most important things for the boards of railway European countries is unification the systems of rail transport, in particular unification the signaling systems and control of the rail traffic. A good solution is as soon as possible implementation the European Rail Traffic Management System (ERTMS), which connect the system of safe operation of trains ETCS and the digital Global System for Mobile Communications – Railways GSM-R.
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Aohua, Guo, Vladimir Zyryanov, and Liu Chunguang. "Development of distributed and decentralized intelligent transport systems in terms of digitalization." E3S Web of Conferences 403 (2023): 07035. http://dx.doi.org/10.1051/e3sconf/202340307035.

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Intelligent Transport System (ITS) is one of the fastest growing areas of intelligent systems that improves road safety and fuel efficiency, reduces traffic congestion, refines the environmental situation of metropolitan cities. The main functions of the intelligent transportation system are real-time monitoring of road conditions in certain areas, location of road traffic incidents (RTI), dynamic monitoring and management of the continuous use of public transport services, as well as anything that can lead to changes in road traffic. In this article, the aforementioned ITS functionality is used as basic simulation scenarios for the development and implementation of an Intelligent Transport System based on the Internet of Things and blockchain technology. Both of these technologies have the inherent characteristics of a distributed technology, integrating IoT sensor nodes and distributed registry technology to register changes in Intelligent Transportation systems and create a credit token mechanism to pay for the use of road transport services. The Intelligent Transport System is a comprehensive intelligent system; it can significantly change and improve traffic conditions.
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Atef Gharbi. "Intelligent Elevator Control Using Decentralized Multi-Agent Systems." Journal of Electrical Systems 20, no. 9s (2024): 3038–46. http://dx.doi.org/10.52783/jes.6482.

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A multi-agent system (MAS) framework optimizes elevator operations in high-rise buildings with dynamic traffic patterns and fluctuating passenger demands. With the proposed MAS approach, which utilizes fully decentralized decision-making and dynamic task allocation, scalability, responsiveness, and energy efficiency are significantly improved. Comparing MAS to traditional centralized systems, simulation results show that it reduces average waiting times by up to 25% while maintaining high performance. MAS eliminates bottlenecks and central points of failure, allowing real-time adaptation to traffic changes and passenger behavior. This approach provides significant advantages over existing elevator control strategies, positioning it as an intelligent building management solution of high effectiveness.
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Anees, Khan, and Sarwes Site Prof. "Study of Congestion Control Scheme with Decentralized Threshold Function in VANETs." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 398–401. https://doi.org/10.5281/zenodo.3589841.

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With the constant increase in vehicular traffic, existing traffic management solutions have become inefficient. Urbanization has led to an increase in traffic jams and accidents in major cities. In order to accommodate the growing needs of transport systems today, there is a need for an Intelligent Transport System. Vehicular Ad hoc Network VANET is a growing technology that assists in Intelligent Transport Systems. VANETs enable communication between vehicles as well as fixed infrastructure called Road Side Units RSU . We propose a distributed, collaborative traffic congestion detection and dissemination system that uses VANET. Each of the driver's smart phones is equipped with a Traffic App which is capable of location detection through Geographic Position based System GPS . This information is relayed to a remote server which detects traffic congestion. Once congestion is confirmed the congestion information is disseminated to the end user phone through RSUs. The Mobile App transmits the location information at periodic intervals. Using the latitude, longitude and the current time, the location of each vehicle is traced. Using location information, the distance moved by the vehicle at a given time is monitored. If the value is below a fixed threshold, congestion is suspected in a particular area. If many vehicles in the same area send similar messages, traffic congestion is confirmed. Once traffic congestion is confirmed, the vehicles approaching the congested area are informed about the traffic through display boards that are available in the nearest RSUs traffic signals . The congestion information is also made available through the Mobile App present in vehicles approaching the congested area. The approaching vehicles may take diversion and alleviate congestion. Anees Khan | Prof. Sarwes Site "Study of Congestion Control Scheme with Decentralized Threshold Function in VANETs" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25324.pdf
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Khachatryan, Nerses. "About some approaches to real-time railway freight management." Herald of CEMI 7, no. 2 (2024): 0. http://dx.doi.org/10.33276/s265838870031789-0.

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The methodology of constructing macroscopic models describing the process of organizing railway freight transportation in real time is presented. One of the key aspects of this methodology is the management of the intensity of freight traffic depending on the workload of stations. This functionality allows the freight transportation system to adjust the speed of trains, optimizing the intensity of freight traffic in accordance with the current situation. Dynamic traffic management helps reduce congestion, optimizes train movement processes and increases the overall efficiency of rail transportation.
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Vieira, Manuel Augusto, Manuela Vieira, Gonçalo Galvão, Paula Louro, and Mário Véstias. "Unlocking Traffic Control: Exploring Intersection Dynamics with Visible Light Communication." EPJ Web of Conferences 305 (2024): 00029. http://dx.doi.org/10.1051/epjconf/202430500029.

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This paper introduces the use of Visible Light Communication (VLC) to enhance traffic signal efficiency and vehicle trajectory management at urban intersections. By integrating VLC localization with learning-based traffic signal control, a multi-intersection traffic system is proposed. VLC enables communication between connected vehicles and infrastructure through mobile optical receivers. The primary objectives are to reduce waiting times and improve overall traffic safety by accommodating diverse traffic movements during multiple signal phases. Cooperative mechanisms and queue/response interactions balance traffic flow between intersections, enhancing road network performance. A reinforcement learning scheme optimally schedules traffic signals, with agents at each intersection using VLC-enabled vehicle communication to improve traffic flow and overall system optimization. Evaluated using the SUMO urban mobility simulator, the system demonstrates reduced waiting and travel times. The decentralized and scalable nature of this approach highlights its potential applicability in real-world traffic scenarios.
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de Zarzà, I., J. de Curtò, Juan Carlos Cano, and Carlos T. Calafate. "Drone-Based Decentralized Truck Platooning with UWB Sensing and Control." Mathematics 11, no. 22 (2023): 4627. http://dx.doi.org/10.3390/math11224627.

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Truck platooning is a promising approach for reducing fuel consumption, improving road safety, and optimizing transport logistics. This paper presents a drone-based decentralized truck platooning system that leverages the advantages of Ultra-Wideband (UWB) technology for precise positioning, robust communication, and real-time control. Our approach integrates UWB sensors on both trucks and drones, creating a scalable and resilient platooning system that can handle dynamic traffic conditions and varying road environments. The decentralized nature of the proposed system allows for increased flexibility and adaptability compared to traditional centralized platooning approaches. The core platooning algorithm employs multi-objective optimization, taking into account fuel efficiency, travel time, and safety. We propose a strategy for the formation and management of platoons based on UWB sensor data with an emphasis on maintaining optimal inter-vehicle secure distances and compatibility between trucks. Simulation results demonstrate the effectiveness of our approach in achieving efficient and stable platooning while addressing the challenges posed by real-world traffic scenarios. The proposed drone-based decentralized platooning system with UWB technology paves the way for the next generation of intelligent transportation systems that are more efficient, safer, and environment friendly.
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M., Bharathwaj, Arunkumar J., Ashwinsanjay J., Kamesh C., and Salini R. "AI Enabled Next-Generation Traffic Control System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 127–33. http://dx.doi.org/10.17762/ijritcc.v11i11s.8078.

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Traffic is one of the superior problems in modern metropolis. Fresh and advanced technology related infusions are required to supervise themselves and direct traffic signals in order to decrease the snarl-upping of traffic. Major problem is when it comes to a predicament or an emergency circumstance which affects the servicing facilities like ambulances, fire trucks, police vans etc. In this paper, we capture data from the surveillance camera and using it we will train the machine using Machine Learning and Deep Learning. So, the process goes where we use a collective number of images which can be enormous in numbers which can be used to train the model. Subsequently, the vehicles are identified, and are categorized into various classes and this classification is done by itself, as it is edified to precision. We procured 88% accuracy using YOLOv5 for vehicle recognition. Further it contributes to the future, so that road design and scrutiny can be developed and secondly the fuel usage can be controlled, and the standby time is also saved effectively. Within some period, we will be able to harmonize most of the signals, by imparting a flexible traffic management system, thus resulting in declination of traffic congestion.
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de Curtò, J., I. de Zarzà, Juan Carlos Cano, Pietro Manzoni, and Carlos T. Calafate. "Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring." Electronics 12, no. 24 (2023): 4913. http://dx.doi.org/10.3390/electronics12244913.

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The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.
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D'Ariano, Andrea, Ingo A. Hansen, and Robin Hemelrijk. "RAILWAY DYNAMIC TRAFFIC MANAGEMENT: APPLICATION OF A TRAIN RESCHEDULING SYSTEM REDUCING DELAYS." IFAC Proceedings Volumes 39, no. 12 (2006): 337–42. http://dx.doi.org/10.3182/20060829-3-nl-2908.00059.

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He, Wei, Tao Lu, and Cheng Qiang Yu. "A Novel Traffic Flow Forecasting Method Based on the Artificial Neural Networks and Intelligent Transportation Systems Data Mining." Advanced Materials Research 842 (November 2013): 708–11. http://dx.doi.org/10.4028/www.scientific.net/amr.842.708.

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Useful information often hides in traffic management system. To mine useful data, prior knowledge has been used to train the artificial neural network (ANN) to identify the traffic conditions in the traffic information forecasting. Subjective information has hence been introduced into the ANN model. To solve this problem, a new ANN model is proposed based on the data mining technology in this work. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) to validate the performance of the proposed ANN model. The results show that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%.
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et al., Palant. "Organizational and economic mechanism of the automatic underground train operation system." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 11 (2021): 37–43. http://dx.doi.org/10.21833/ijaas.2021.11.005.

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This work deals with the provision of underground, as one of the main modes of public transport, with automated control systems, in particular the provision of automated train management. It defines that the underground is one of the most progressive modes of transport, which is related to its environmental safety, comfort, and speed of movement, as this mode of transport does not intersect with other transport and pedestrian routes, which excludes traffic blocks. Underground is an important element of urbanized spaces, for it serves large passenger traffic. The article presents an algorithm for the creation of an automated control system for the underground train system. Based on this algorithm, a model of building an organizational and economic mechanism for automation of control systems has been developed. The diagram of organizational and technical implementation of the automated system of underground train management is presented. An economic analysis of the effectiveness of the application of the automated control system of underground trains was carried out. The recoupment on the acquisition and installation of automation systems is very high. The application of the automated system will increase the capacity of the underground by optimizing the traffic schedules, which will contribute to increasing its profitability. It is also useful to determine the reduction of electricity consumption due to the change in the dynamics of acceleration and braking systems, which is important, as the underground is a powerful enterprise. In the future, a promising direction is to streamline the coordination of train schedules and other modes of land public transport.
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Eremina, Luba, Anton Mamoiko, and Guo Aohua. "Application of distributed and decentralized technologies in the management of intelligent transport systems." Intelligence & Robotics 3, no. 2 (2023): 149–61. http://dx.doi.org/10.20517/ir.2023.09.

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Shifting focus from the field of distributed and decentralized technologies in the management of intelligent transportation systems (ITS), we now delve into the specific application of blockchain in transportation management. Blockchain is a fundamental component of distributed and decentralized technologies. The research paper discusses the utilization of blockchain technology in managing transportation systems through multi-agent systems. Specifically, the use of blockchain technology is examined in the context of the quick road system (QRS) in ITS to provide a service for obtaining a special fare status. This service aims to establish a decentralized network that facilitates real-time road lane sharing. The study indicates that depending on traffic situations, drivers can share their lane space with other vehicles traveling on the same route by exchanging incentives via blockchain with other private car owners, thereby allowing for faster travel for individuals in a hurry or those requiring priority access to fast lanes. The paper also addresses the increasing number of connected devices in ITS due to the development of the internet of things (IoT) technology. It highlights the importance of efficiently utilizing large datasets and identifies the internet of vehicles (IoV) as a crucial area of integration for existing IoT technologies to address smart traffic within multi-agent systems.
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Naghiyev, Arzoo, Sarah Sharples, Brendan Ryan, Anthony Coplestone, and Mike Carey. "Expert knowledge elicitation to generate human factors guidance for future European Rail Traffic Management System (ERTMS) train driving models." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 231, no. 10 (2017): 1141–49. http://dx.doi.org/10.1177/0954409717695902.

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The European Rail Traffic Management System (ERTMS) will have an impact on the train driving task and the behaviour of the train drivers. This paper presents a part of the final study of the series of studies that have been conducted as part of a three-year research project, which investigated the effects of ERTMS on the behaviour of the train drivers. In recent times, a number of models of train driving have been developed in order to inform the design of the train driving technology and understand the implications of the design of the train driving task on the performance of the drivers and their behaviour. An expert knowledge elicitation study was conducted to evaluate the existing train driving models and to generate a guidance on human factors for the future ERTMS train driving models. The study consisted of a workshop, which was conducted using the rail human factor experts who have been directly involved with the projects examining ERTMS. The current train driving models were evaluated and ideas were generated as to how these should be shaped in the future to include systems such as ERTMS. The findings of the study contributed to bridging the gap between the theoretical understanding of the cognitive strategies of the train drivers and the practical implementation of novel technologies by the rail industry.
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Hashemi, Seyed Mohammad, Ruxandra Mihaela Botez, and Georges Ghazi. "Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl." Algorithms 16, no. 10 (2023): 472. http://dx.doi.org/10.3390/a16100472.

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This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs.
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Singh, Astha, and Pawan Singh. "License Plate Recognition for Traffic Management." Journal of Management and Service Science (JMSS) 1, no. 2 (2021): 1–14. http://dx.doi.org/10.54060/jmss/001.02.001.

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The objective of this report is to present an overview of the project, license plate recognition. This system is to basically detect a vehicle using the information of the license plate in order to use the information at various valid sites. This tool can work as a part of other big projects in the industry for security purposes as well as for analysis purposes. There is a detailed insight of the project in several different chapters throughout the report. This project is based on detection and recognition algorithms; using several libraries of python to work on images and videos and thereafter using the processed image to further train and test a model using machine learning algorithm such that the recognition is done with higher accuracy. The beginning outlines the introduction to the topic and its importance in the real world by highlighting some other applications using similar approach. Later it highlights the technology and skills in use in order to completely deploy the idea. Further there is an explanation to the feasibility study as well as the requirement specification while system design revolves around the basic designing of several modules which would integrate to work as the whole system and test cases. Implementation and testing approach is being discussed and thereby light has been thrown on the results and conclusion thus bringing attention to system’s limitation and its future scope.
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Vieira, Manuel Augusto, Gonçalo Galvão, Manuela Vieira, Paula Louro, Mário Vestias, and Pedro Vieira. "Enhancing Urban Intersection Efficiency: Visible Light Communication and Learning-Based Control for Traffic Signal Optimization and Vehicle Management." Symmetry 16, no. 2 (2024): 240. http://dx.doi.org/10.3390/sym16020240.

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This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.
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Sattorov, Samandar, Alexey Kotenko, Shukhrat Saidivaliev, Jamshid Barotov, and Nassim Rustamov. "Development of the fundamentals for organizing the transportation of local goods on the railways of the republic of Uzbekistan." E3S Web of Conferences 460 (2023): 06015. http://dx.doi.org/10.1051/e3sconf/202346006015.

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Target: The article discusses the approach to variant calculations of a phased increase in line capacity, modeling of a traffic management system aimed at meeting transportation needs and finding a balance between schedules of various categories of trains in the train schedule configuration to improve the efficiency of local freight transportation. Methods: The article uses methods from the theory of operational work, development of train schedules, graph theory, methods of system and cluster analysis, mathematical statistics, economic and mathematical modeling. Results: two-level model of transport and logistics regional network; iterative algorithm for assigning reference stations; approach to calculating the optimal weight of a local train; algorithm and recommendations for drawing up daily train schedules with local cargo. Practical significance: the results will make it possible to develop rational options for the movement of trains with local cargo on schedule in conditions of increasing transit freight traffic.
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Lucia, Walter, Giuseppe Franzè, and Domenico Famularo. "A receding horizon event-driven control strategy for intelligent traffic management." Discrete Event Dynamic Systems 31, no. 3 (2021): 469–88. http://dx.doi.org/10.1007/s10626-020-00337-7.

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AbstractIn this paper, the intelligent traffic management within a smart city environment is addressed by developing an ad-hoc model predictive control strategy based on an event-driven formulation. To this end, a constrained hybrid system description is considered for safety verification purposes and a low-demanding receding horizon controller is then derived by exploiting set-theoretic arguments. Simulations are performed on the train-gate benchmark system to show the effectiveness and benefits of the proposed methodology.
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Saad, Soha, Florence Ossart, Jean Bigeon, Etienne Sourdille, and Harold Gance. "Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study." Energies 14, no. 19 (2021): 6420. http://dx.doi.org/10.3390/en14196420.

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The adjustment of rail traffic in the event of an electrical infrastructure disruption presents an important decision-making process for the smooth operation of the network. Railway systems are complex, and their analysis relies on expensive simulations, which makes incident management difficult. This paper proposes the use of sensitivity analysis in order to evaluate the influence of different traffic adjustment actions (e.g., spacing between trains and speed reduction) on the train supply voltage, which must never drop below the critical value prescribed by technical standards. Three global sensitivity analysis methods dedicated to black box, multivariate, nonlinear models are considered: generalized Sobol indices, energy distance-based indices, and regional sensitivity analysis. The three methods are applied to a simple traffic rescheduling test case and give similar results, but at different costs. Regional sensitivity analysis appears to be the most suitable method for the present application: it is easy to implement, rather fast, and accounts for constraints on the system output (a key feature for electrical incident management). The application of this method to a test case representative of a real rescheduling problem shows that it provides the information needed by the traffic manager to reschedule traffic in an efficient way. The same type of approach can be used for any power system optimization problem with the same characteristics.
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Rozenberg, Efim, Aleksey Markov, and Aleksey Ozerov. "Digital Technologies for Train Passage through Sections and Stations — the Basis for Organizing Traffic on Moscow Central Diameters." Transport automation research 9, no. 4 (2023): 324–32. http://dx.doi.org/10.20295/2412-9186-2023-9-04-324-332.

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train separation, automatic block system, ABTC-MSh system, moving block, interlocking, automatic cab signalling, train control system The article discusses the development of automatic block systems, which form the basis of separation systems. It also highlights the key factors limiting the capacity on open lines, and ways to solve them. A brief overview of the foreign experience of the organization of train separation using automatic block system based on track circuits is given. The article shows the capabilities and role of the computerbased automatic block signalling system with a centralized placement of equipment, audio frequency track circuits and duplicating information transmission channels as an important element of the train traffic management at the sections of Moscow Central Diameters. The significance of the implemented non-stop passage of trains along the main station tracks providing substantial section capacity increase is demonstrated. The necessity of building a digital system for train spacing is shown, which makes it possible to implement the moving block technology with-in the station ensuring the movement of trains on open lines and at stations with the same headway. The advantages of a new type of the computer-based automatic block signalling system as a basic element of a promising Russian train traffic control system and the positive effects of its implementation on the Russian railway network are indicated. The direction of development of train separation systems is outlined within the framework of further digitalization.
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Karthikeyan, P., Wei-Lun Chen, and Pao-Ann Hsiung. "Autonomous Intersection Management by Using Reinforcement Learning." Algorithms 15, no. 9 (2022): 326. http://dx.doi.org/10.3390/a15090326.

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Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake-safe control module is utilized to make sure that each vehicle travels safely, and we train the system to acquire an effective model by using reinforcement learning. We have simulated our proposed method by using a simulation of urban mobility tools. Experimental results show that our approach outperforms the traditional method.
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Thanusree, Tulasi, Gunti Sai Chaitanya, Yama Ruth Elizabeth, V. Indrani, and A. Prasanthi. "Intelligent Transportation System Using Multi Stream Feature." ITM Web of Conferences 74 (2025): 03004. https://doi.org/10.1051/itmconf/20257403004.

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Traffic flow prediction accuracy is very important for intelligent transportation systems (ITS). Many studies have proposed different methods for traffic flow prediction including ARIMA, ANN and SVM. With the development of deep learning technology, the evolutionary models of RNN such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models have been found to perform well in traffic flow prediction. This paper aims at investigating the use of the Random Forest Regressor Model, an ensemble learning algorithm, for improved and accurate traffic prediction. Random Forest algorithm is highly robust and is well suited for large datasets with many features, thus making it suitable for traffic forecasting. In this research, historical traffic data is used to train the model together with other variables such as traffic flow, weather and time. The Random Forest model performance is compared with the traditional prediction methods using Mean Squared Error. It shows that the Random Forest model is better than the conventional methods and can give better accurate forecasts of traffic flow and can be used in real time traffic management. It presents the actual and predicted vehicle count per hour.
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Vieira, Manuela, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Mário Véstias, and Pedro Vieira. "Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance." Vehicles 6, no. 2 (2024): 666–92. http://dx.doi.org/10.3390/vehicles6020031.

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This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.
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Morin, Xavier, Nils O. E. Olsson, and Albert Lau. "Managerial Challenges in Implementing European Rail Traffic Management System, Remote Train Control, and Automatic Train Operation: A Literature Review." Future Transportation 4, no. 4 (2024): 1350–69. http://dx.doi.org/10.3390/futuretransp4040065.

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This paper explores the management of digitalization projects within the railway industry. It aims to increase and understand the opportunities presented by digitalization and automation in rail operations. Employing a scoping review methodology, this research investigates the execution of European Rail Traffic Management System (ERTMS), remote train control (RTC), and automatic train operation (ATO) projects spanning from 2005 to 2023, with a particular emphasis on metro automation, the remote control of freight and passenger trains, fully automated trains, and highly assisted driving. The refined selection process yielded 30 papers. The analysis of the retrieved papers identified managerial issues, with stakeholder management, change management, and organizational management emerging as recurring themes. Despite the increasing trend in publications, the limited representation managerial issues in ERTMS, RTC, and ATO projects in scientific research persists, with implications for the industry’s advancement. This research sheds light on the critical intersection of change management and digitalization within the railway industry by showing the impact of ERTMS, RTC, and ATO on organizational and scope dynamics. The need for human-centered systems is highlighted, showing the necessity of involving every echelon of the organization in the change management process. These findings provide insights for practitioners, researchers, and policymakers, emphasizing the need for understanding and addressing managerial aspects for successful and sustainable digitalization implementations.
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Xiaonan, Zhao, and Zhang Yong. "Research on Railway Traffic Conflict Management Method in Onboard-centered Train Control System." IOP Conference Series: Earth and Environmental Science 587 (October 23, 2020): 012020. http://dx.doi.org/10.1088/1755-1315/587/1/012020.

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Ul'yanickaya, Viktoriya, Elizaveta Ivanova, Dar'ya Isakova, and Aleksandra Kishikova. "Creation of Automated Program for Calculating the Values of Target Indicators for Traffic Schedule Implementation." Proceedings of Petersburg Transport University 20, no. 1 (2023): 172–81. http://dx.doi.org/10.20295/1815-588x-2023-1-172-181.

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Purpose: Achievement of the autonomy of calculations of target indicators for the implementation of passenger and suburb train schedule, decision-making and operational management, based on obtained calculations. Writing the program "Calculation of Target Indicator Values for Traffic Schedule Implementation" is due to the need to improve process implementation quality and to obtain maximal efficiency of planning and management system in a whole. Methods: Pascal programming language was used to automate the calculation of target indicators. Results: Creation and writing of independent software product with the preservation of organizational and mathematical methods of the work with information for to make managerial decisions. Practical significance: Automated calculation of target indicators for passenger and suburb train traffic schedule implementation will allow passenger complex all participants to consider the automation of the group of the processes related to information collection and processing on the process implementation "Train Traffic Schedule Implementation" through a single window. The proposed software product can be recommended for practical usage in the passenger complex of JSC "Russian Railways".
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Ramp, Michalis, Andreas Kasis, Charalambos Menelaou, and Stelios Timotheou. "Stability of regional traffic networks employing maximum throughput demand management." European Journal of Control, Special Issue (June 19, 2024): 101061. https://doi.org/10.1016/j.ejcon.2024.101061.

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This paper considers the stability and optimality properties of traffic demand management schemes, motivated by the integration of smart monitoring and control technologies in traffic networks. First, a suitable optimization problem is formulated that aims to obtain demand input values that maximize the throughput within traffic networks adhering to regional traffic dynamics with triangular macroscopic fundamental diagrams. We show that optimal solutions to this problem may lead to unstable behaviour, revealing a trade-off between stability and optimality. To address this issue, we analytically study the stability properties of traffic networks at the presence of constant demand input and provide suitable local conditions that guarantee stability when the system's equilibrium densities are strictly within the free-flow region, but not at the critical density. The latter case is significant, since the maximum throughput behaviour coincides in many cases with the local critical density. We resolve this by proposing a decentralized proportional demand control scheme and suitable local design conditions such that stability is guaranteed. Our analytic results are validated with numerical simulations in a 3-region system that demonstrate the effectiveness and practicality of the proposed approach.
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Dedík, Milan, Vladimíra Štefancová, Jozef Gašparík, Vladimír Ľupták, and Martin Vojtek. "Traffic Capacity Assessment of the Selected Track Section on the Slovak Railways Network after the Implementation of ETCS L3 Based on Signaling Principle." Applied Sciences 12, no. 11 (2022): 5597. http://dx.doi.org/10.3390/app12115597.

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In the field of railway operation, it is essential to establish uniform conditions for interconnectivity requirements and compatibility of equipment in the Pan-European railway area to ensure effective interoperability. It also includes, for example, the introduction of a control system with modern and advanced interlocking systems (safety devices). The European Train Control System (ETCS) is a single European train protection system that will increase safety in rail transport. Nevertheless, this system may have an impact on the throughput on those railway lines where it is applied. The main research objective is to determine the impacts and effects of the configuration of track signaling equipment on the operational management of traffic and especially on the creation of a traffic plan. The optimization of transport processes on the railway infrastructure means creating the conditions for achieving higher throughput performance, especially including a higher number of train paths into the train traffic diagram. This paper examines and compares the impacts of ETCS and its levels (in particular ETCS L3) on the practical throughput of the selected national infrastructure manager. A heuristic procedure is used with the application of the analytical methodology of the Railways of the Slovak Republic (ŽSR), which uses the principles of mathematical statistics and probability. Significant comparative indicators are occupancy times and the degree of utilization of practical throughput. These are used in investment decisions in the modernization of line sections to achieve interoperability of the railway system.
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Antonio, Guillen-Perez, and Cano Maria-Dolores. "AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks." Sensors 22, no. 6 (2022): 2217. http://dx.doi.org/10.3390/s22062217.

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The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5LA can reduce waiting time by more than 99% and time loss by more than 95%.
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G. Divya, Pulgam Manjunath Reddy, Bhukya Muralikrishn A, and Pakala Parsharam. "DEEP CNN FOR SMART MOBILITY: AN AI-BASED TRAFFIC FLOW ANALYZER FOR ADAPTIVE URBAN INFRASTRUCTURE." International Journal of Engineering Research and Science & Technology 21, no. 3 (1) (2025): 47–55. https://doi.org/10.62643/ijerst.2025.v21.n3(1).pp47-55.

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Abstract:
This research introduces a novel hybrid deep learning-based system for intelligent road traffic monitoring, aimed at improving transportation safety through accurate identification and classification of road incidents. The system integrates multiple machine learning models— including a Random Forest Classifier, a Deep Neural Network (DNN), and a hybrid model that combines a Convolutional Neural Network (CNN) with an Extra Trees Classifier (ETC)—to detect and classify incidents such as accidents, dense traffic, fire, obstacles, smoke, and sparse traffic. A comprehensive dataset of road scene images is preprocessed, divided into training and testing sets, and used to train these models. Among them, the hybrid CNN+ETC model achieved the highest accuracy of 95.14%, significantly outperforming the others. The system is equipped with an intuitive graphical user interface (GUI) that allows users to upload datasets, preprocess images, train models, and perform incident predictions on test images. It also offers visualization tools such as accuracy and loss graphs, along with confusion matrices for performance evaluation. The model has shown precise prediction capabilities, correctly identifying scenarios like smoke, accidents, and heavy traffic. This application not only demonstrates strong practical relevance for real-world use but also represents a promising step forward in advancing real-time traffic incident detection and smart transportation management systems.
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