Traffic Control System Research Articles

An Examination of Emergency Vehicle Traffic Management and Control Systems

Emergency response is crucial for keeping the public safe and ensuring the well-being of individuals in critical situations. The prompt and efficient deployment of emergency vehicles is necessary to minimize response times and reduce potential harm to people and property. This comprehensive survey paper explores various approaches, strategies, and technologies used to optimize traffic flow for emergency vehicles. It begins by emphasizing the importance of swift emergency vehicle response, particularly in medical emergencies and law enforcement. The paperthen examines current methodsfor managing traffic and the challenges that hinder the timely movement of emergency vehicles, such as congestion, infrastructure limitations, and communication problems. Additionally, it presents case studies and real-world examples that demonstrate the effectiveness and challenges of different strategies for optimizing traffic flow. The survey also delves into the ethical considerations, privacy concerns, and societal impacts associated with prioritizing emergency vehicles.

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs’ ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generated detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the performance of GPT-4o-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. The GPT-4o-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs’ benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs’ transformative potential for traffic control, enhancing efficiency and safety at intersections.

Design and Implementation of Intelligent Traffic Signal Light Control System based on LoRa Technology

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

Analysis and Research on the Intelligent Control System of Comprehensive Traffic Signals

The existing urban traffic signal control system exhibits certain inefficiencies, including prolonged waiting times and frequent traffic congestion. Research indicates that the integration of neural networks can enhance the effectiveness of intelligent traffic control systems, potentially improving overall traffic flow and reducing delays. Neural network is a highly parallel, nonlinear and highly redundant system. Meanwhile, the self-learning and self-organizing capabilities of neural network have unique advantages for those systems that cannot establish accurate mathematical models. According to the characteristics of complex, variable and random traffic conditions at urban intersections, this paper analyzes the traffic signal control by using neural network control method. This paper introduces a design method of intelligent traffic light based on dynamic time adjustment of traffic flow. By detecting the traffic flow on site, and then allocating the green time of each lane according to the neural network algorithm, the traffic flow dynamic adjustment can be realized, and the traffic capacity of the intersection can be effectively improved. This kind of intelligent control traffic lights can effectively control traffic, and has obvious effects on dreading traffic flow, improving road capacity and reducing traffic accidents.

Intelligent Traffic Light Control System and Application

In recent years, with the surge in the number of vehicles, traffic congestion has become an increasingly severe global issue. So Intelligent Traffic Signal Control Systems have emerged as a critical component of transport systems to address this challenge by optimizing traffic flow, reducing delays, and enhancing road safety. The intelligent system depends on several advanced technologies, such as IoT devices, big data analytics, and artificial intelligence algorithms. Also, the system can dynamically adjust traffic signals based on real-time road conditions via technologies such as Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X) communication, which successfully improves traffic flow during peak hours, and minimizes congestion. The review will also mention successful case studies in cities such as Beijing, where smart systems have significantly improved traffic efficiency. The last part of review will focus on future developments about scalability, cost-effectiveness, and data security to ensure the continued success of these systems in modern urban environments.

Integrated mathematical model of the transcribing system adapted to aviation conditions

In this paper the authors have created the integrated mathematical model of the transcribing system adapted to aviation conditions, taking into account many factors. The paper analyses the following main problems of transcribing Englishlanguage speech between pilots and air traffic controllers (ATC) (radio exchange), namely: the tendency to use abbreviations and specialized vocabulary, which can cause misunderstanding for one of the parties; speech illegibility due to noise in the cockpit or in the radio frequency zone; insufficient clarity and accuracy in expressing instructions by air traffic controllers can lead to errors in the understanding and execution of instructions by pilots; limitations in the availability of communication channels and their overloading; lack of training in the use of English-language terms and expressions in the air traffic control system. Inadequate training in English language terms and expressions can lead to difficulties in understanding instructions and messages between pilots and air traffic controllers; differences in accents and pronunciation of communicators can also cause difficulties in speech comprehension. Aviation communication errors are critical to aircraft safety. The ambiguity of certain phrases or expressions in English can lead to misinterpretation and misunderstanding of instructions by controllers; lack of context or lack of information about the current situation on board the aircraft can make it difficult to transcribe speech and lead to misunderstanding of messages; use of slang or informal expressions can make transcribing English-language speech more difficult and cause misunderstandings; lack of opportunity to ask clarifying questions or request a real-time repetition of a message can lead to misunderstandings; and the use of slang or informal expressions can lead to misunderstandings. Even the most minor errors can have disastrous consequences. The analysis revealed that in the overwhelming majority of cases it is linguistic factors that cause misunderstandings between participants in radio conversations, which is evidence of the need to develop and improve this model.

Traffic Light Controller System Analysis based on FPGA

Traffic Light Controller System Analysis based on FPGA

Enhanced Air Traffic Control Safety Using low Earth orbit Satellites: Non-TCAS Aircraft

This paper presents a study on a novel TCAS design that combines the exploitation of low-orbit satellites with the existing TCAS system to improve operational efficiency and overcome challenges. With the continuous growth of air traffic, ensuring safety remains a top priority. TCAS was developed to mitigate the risks of aircraft collisions and is mandatory on large transport aircraft. TCAS uses information and data to determine the altitude and relative positions of nearby aircraft. However, despite the advances in air traffic control (ATC) systems, non-TCAS-equipped aircraft continue to operate in the airspace, potentially increasing the risk of mid-air collisions. In addition, existing TCAS systems often generate frequent and unnecessary alarms, especially in densely populated terminal areas, leading to erroneous pilot actions. The proposed solution aims to detect non-TCAS-equipped aircraft by other aircraft, whether they are equipped with TCAS or not. Therefore, the goal is to optimize the efficiency of TCAS to reduce the risk of mid-air collision and improve overall aviation safety. The management applications are distributed on the cloud to save resource exploitation, including energy consumption by processing and air traffic control-related exchanges.

Algorithm performance study pulse suppression of auto-oscillation phenomena in the electromechanical system of the electric traction drive in braking mode electric traction drive system in braking mode by methods of simulation modeling

Research relevance. Modes accompanied by increased sliding of a wheel with negative viscous friction, characterized by reduction of friction force at growth of sliding speed, can arise at the movement of the vehicle. In these cases, a loss of stability may occur, resulting in the excitation of auto-oscillation phenomena in the electromechanical system (road-wheel-mechanical drive-electric motor). The emergence of this process sharply increases the dynamic load of the system, which can lead to its failure or breakdown. As a consequence, the development of suppression methods of auto-oscillation phenomena is considered to be an urgent task.Purpose of the research. Verification of operability and efficiency of the suppression method of auto oscillations in the electromechanical system of wheel drive during braking of the vehicle.Materials and Methods. With the help of Lyapunov’ function analysis for the electromechanical system of wheel drive the suppression method of auto oscillation phenomena is substantiated. The study of the algorithm performance has been carried out with the use of MATLAB Simulink program complex.Research results. The operability and efficiency of the algorithm is proved by methods of simulation mathematical modeling, which allows its further use in the development of traffic control systems. In case of braking of the vehicle equipped with antilock braking system with the function of suppression of auto oscillations on a slippery support base the reduction of amplitudes oscillation of angular velocities of wheels by 80% and braking moments by 96% is observed. When this vehicle is braked on a high adhesion base, the amplitudes are reduced by 98% in angular velocities and 81% in torques. In simulation modeling of vehicle braking dynamics on a support base with low adhesion properties it has also been shown that in these cases the evasive maneuver can be performed, which indicates on the increase in controllability and active safety of the vehicle.Conclusion. The practical value of the study lies in the possibility of using the developed algorithm for suppressing auto oscillations in practical application as part of vehicle control systems. The developed algorithm can be used on vehicles of different classes equipped with individual traction electric drive of driving wheels.

Towards Safer Roads: An Automated Traffic Control System with Enhanced Vehicle Speed Detection and Measurement Algorithm

Towards Safer Roads: An Automated Traffic Control System with Enhanced Vehicle Speed Detection and Measurement Algorithm

Leveraging Artificial Intelligence for Enhancing the Resilience and Security of Critical Infrastructures in the United States

In the rapidly evolving landscape of global security, the United States faces increasingly sophisticated threats to its critical infrastructures and national security. These threats emanate from state and non-state actors employing advanced technologies to disrupt, degrade, and destroy essential systems. In response, Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the resilience and defense mechanisms for critical infrastructures operation. This research paper explores the potential and application of AI in safeguarding the nation's critical assets, including energy grids, transportation networks, gas & oil pipelines, communication systems, financial institutions, water supply systems, healthcare databases, IT networks, and air traffic control systems. By leveraging machine learning algorithms, predictive analytics, and anomaly detection techniques, AI can identify and mitigate vulnerabilities in real-time, preemptively countering cyber-attacks, physical sabotage, and air traffic control disruptions. Additionally, AI-driven systems bolster cybersecurity, ensuring the resilience and security of vital US systems against emerging cyber threats. Furthermore, AI enhances decision-making capabilities, providing security agencies with actionable intelligence and situational awareness, while also contributing to overall security enhancements. This paper examines the ethical considerations, challenges, and future directions of integrating AI into national security frameworks. Through a comprehensive analysis, this study underscores the vital role of AI in fortifying the United States' critical infrastructures against the growing array of adversarial threats.

AI-Based Adaptive Traffic Management

Traffic congestion is one of the biggest and most important issues facing modern city dwellers, necessitating effective and efficient solutions. AI-powered traffic light optimization has grown in popularity. The available literature indicates that intelligent approaches outperform traditional management techniques. The amount of cars on each intersection road determines the signal timings, which are adjusted to reduce traffic at the intersection. The recommended topology may significantly reduce traffic jams and wait times at the intersection. A lot of benefits can arise from an efficient traffic control system, such as less traffic on the roads, more economic efficiency, and enhancements in pedestrian safety and air quality. This paper discusses the several phases of the process, including data collecting, data analysis, and simulation development. At the conclusion of the process, a Python simulation model is created to test the topology.

Assessing digitalization maturity in port environments: a comprehensive tool for logistics, operations, and planning management

ABSTRACT This research presents a tool for assessing the digitalisation maturity of ports, focusing on logistics, operations, and planning and management. Through a systematic literature review and the MethodiOrdinatio methodology, a theoretical model was developed to evaluate digital maturity in port environments. The review revealed critical technologies in port operations, leading to the creation of a maturity assessment tool. This tool was validated by domain experts and applied to a major Brazilian port. The assessment showed that the port achieved a maturity level of 1, indicating high proficiency in operations, especially in technology for maintenance. Key implementations included advanced cybersecurity measures and effective integration of systems for gate management, scheduling, and traffic control. The study underscores the tool’s practicality and efficiency, with findings confirming its capability to enhance port digitalisation strategies.

Design and Implementation of Device Circuit of Microcontroller-Based Smart Traffic Light Control System

In this paper, the design and implementation of a microcontroller-based smart traffic light control system is presented. The proposed smart traffic light is meant to mitigate or entirely eliminate hassles encountered by road users, mostly at road intersections. The system is sensitive to road density and therefore operates in smart and normal modes, with normal mode as the default; however, it switches to smart whenever the need arises. The approach adopted includes; typical intersection design, circuit diagram design, circuit analysis and overall system development and implementation. The microcontroller, which serves as the overall system brain is programmed using proteus electronic software; and then interfaced with already calibrated ultrasound modules and other components. Afterwards, it is initiated to confirm that research objectives have been met. The design employs a crossbar to prevent drivers from using a lane when not supposed. The traffic light control system, if implemented would significantly minimize traffic frustrations and ugly experiences road users encounter daily on Nigeria roads. However, this work is centred more on circuit operation and design, circuit analysis and overall system development and implementation.

Review of modern technologies of vessel monitoring in the water areas of the inland waterways using video surveillance tools

At present, there is a problem on inland waterways located within the boundaries of large settlements to ensure monitoring of vessel traffic, including small vessels. This situation is due to limitations on the use of traditional means of monitoring of inland waterway vessels, caused primarily by restrictions on the use of radar systems and radiotechnical means operating in the ultrashortwave frequency range within the city limits. At the same time in large cities of the Russian Federation implemented and successfully used video surveillance systems. The system operating in the city of Moscow to monitor the water area within the city as part of the city traffic control system is considered. Most of the sections of inland waterways located within the limits of St. Petersburg fall within the range of the cameras. The article considers possible approaches to use the city video surveillance system for monitoring the water area within the city limits of St. Petersburg. The paper describes technologies based on artificial neural networks potentially suitable for identification of ships and determination of their exact location at a given moment of time. Advantages and disadvantages of the considered methods are analyzed, as well as a variant of solving the inverse problem of bearing of ships (from the shore) using video surveillance systems consisting of two cameras is proposed. The algorithm of the system operation for identification and determination of vessel movement parameters is proposed. Domestic and foreign experience of solving the problems of vessel identification using video surveillance systems, as well as determining the parameters of vessel movement is analyzed. Several neural networks (object detection, text recognition) are proposed as a solution as a basis for further study of the described problem.

Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

Exploring Artificial Intelligence PEAS Framework for Enhanced Decision-Making

An agent can refer to any device employed as a sensor to detect environmental elements and entities, providing responses based on that information. The cycle of agents can include perception, action, processing, and performance, while the environment around us is populated with agents such as temperature sensors, CCTV cameras, mobile phones, and more. Humans, software, and robots around us also function as AI agents. Using artificial intelligence we can create advanced systems with human-like behaviour. This research study represents a comprehensive view of existing literature and an analysis of methods designed to enhance Artificial Intelligence decision-making possibilities. By studying the facts and details of PEAS models of AI, a better idea can be gained on how AI can make decisions similar to human intelligence. This study includes a literature review of some related research. Objective of this study is to discuss the framework, elements, and challenges of the PEAS model in Artificial Intelligence and to simulate the model of control agents for traffic light control systems, its framework with entities and parameters, and limitations.

Smart Traffic Light Control System

Abstract—Traffic congestion is a significant issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. This project introduces an adaptive Traffic Light Control System that dynamically prioritizes traf- fic direction based on real-time vehicle counts obtained from cameras. The system ensures efficient traffic flow by allocating green light duration proportionally to the vehicle density in each direction while maintaining fairness through a minimum green time of 3 seconds for all directions. The implementation includes a clockwise rotation of the green signal, preventing any direction from being perpetually delayed. The maximum green light duration is capped at 75 seconds to maintain system balance and efficiency. The results demonstrate improved traffic clearance, reduced waiting times, and significant optimization in signal utilization, making this system an effective solution for modern traffic management challenges. Index Terms—Smart Traffic Light, Traffic Light, OpenCV, Yolov.

Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery.

Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog.