Intelligent Traffic Management System Research Articles

Intelligent Traffic Management System: Detecting Over Speeding, Red-Light Violations, Helmet Violations, and Triple Riding

Abstract: According to recent reports, traffic violations have mostly resulted in an increase in fatalities and injuries on Indian roads. Because manually identifying traffic violations takes time, an automatic computer vision-based object identification model was required. The fundamental idea behind this research is to identify many transgressions using a single video frame. To perform various activities, the security camera's input video stream is processed and annotated. COCO is the dataset utilized for red-light leaping, while Google pictures are annotated to provide the dataset for over boarding. Tensorboard is used to train the model and visualize its results. The criteria employed include precision, recall, Fmeasure, and Pmeasure. Red light skipping accuracy is 93%, and the over boarding mAP value is 0.5:0.95. This system makes extensive use of the video feed to detect various forms of breaches.

Development Status of Intelligent Traffic Management System

The intelligent traffic management system is the most important part of the intelligent traffic system. The research and analysis of the development status of the intelligent traffic management system can provide a research basis for its future development. This paper studies the three subsystems of the intelligent traffic management system: the intelligent traffic monitoring system, the By comparing the research methods used by scholars in different time periods for intelligent information service systems and signal control systems, it is found that with the application of technologies such as the Internet of Vehicles, big data, Internet of Things, and deep learning, the level of intelligent traffic management system management is constantly rising.

Vision based intelligent traffic light management system using Faster R‐CNN

AbstractTransportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Interoperability for intelligent traffic management systems in smart cities

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.

Intelligent traffic management system of a big city: ontology concept “decision models”

The problem of traffic jams is still relevant for the transport system of many large cities on different continents. The work is aimed at the formation of an ontologically controlled technology, which is based on the use of available means of data production, information resources, modelling of traffic forecasting of large cities and traffic management based on these forecasts. That is why it pays great attention to the research of innovative solutions to the problem of optimizing traffic through the city network using big data, artificial intelligence models, and Internet of Things technologies. At the same time, the main aspects that require human intervention in the development of the reference ontology of the “Road Traffic” domain are defined. The scientific novelty of the work arises in the creation of the ontology concept “Decision Model”, which will later become the basis of intelligent informational technology for effective traffic management in large cities. This development is crucial for solving traffic congestion problems by intelligently distributing traffic flows through the city road network, as it unifies the knowledge representation. The attention is focused on the scheme of formation of a hybrid model of decisions, which are at various stages of execution of the control actions of the system. The task that will be solved by the artificial intelligence model is defined. The possibility of using the ant colony algorithm to find the shortest path is also shown.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

A Comparative Analysis of Traditional Roadways vs. Electric Vehicle (EV) Roads: Environmental, Infrastructure, And Technological Perspectives

As the global transportation sector seeks to address pressing environmental challenges and transition towards sustainable mobility solutions, the comparative analysis between Traditional roadways and Electric Vehicle (EV) roads emerges as a critical area of investigation. This paper presents a comprehensive comparative analysis of traditional road infrastructure and EV road infrastructure from environmental, infrastructure, and technological perspectives. From an environmental standpoint, the analysis evaluates the life cycle environmental impacts, including greenhouse gas emissions, air and noise pollution, and resource consumption associated with traditional roadways and EV roads. It examines the potential for EV roads to mitigate environmental impacts through reduced fossil fuel dependency, lower emissions, and integration with renewable energy sources. Infrastructure comparisons encompass the design, construction, maintenance, and operation of traditional roadways versus EV roads. Factors such as pavement materials, structural integrity, durability, and lifecycle costs are scrutinized to assess the performance and resilience of each infrastructure type. Additionally, the analysis explores the implications for urban planning, land use, and transportation networks in accommodating EV road infrastructure. Technological perspectives delve into the advancements and innovations driving both traditional roadways and EV roads. It examines the integration of smart technologies, intelligent traffic management systems, and renewable energy solutions in enhancing the functionality, efficiency, and sustainability of road infrastructure. Furthermore, the analysis explores the implications of EV road technologies, such as dynamic wireless charging systems and vehicle-to-grid communication, on future transportation systems. Through a systematic comparison of traditional roadways and EV roads, this study provides insights into the environmental, infrastructure, and technological implications of each infrastructure type. By elucidating the benefits, challenges, and opportunities associated with EV road adoption, policymakers, urban planners, and transportation stakeholders can make informed decisions to advance sustainable transportation solutions and mitigate environmental impacts in the transition to electric mobility.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

A multi-level privacy-preserving scheme for extracting traffic images

Traffic images are constantly used as a stick to assess traffic conditions. Traffic flow statistical analysis, road condition safety monitoring, vehicle violation detection, accident surveillance, and the driving environment perception of autonomous vehicles are all functionalities that depend on the processing of traffic images. However, traffic images contain privacy-sensitive information related to users, such as license plate numbers, drivers and passengers. Extracting, analyzing and sharing such privacy-sensitive information without any security measures may raise concerns among users about potential privacy violations. This paper proposes a multi-level privacy protection scheme for traffic image extraction based on compressive sensing, which has the advantages of data undersampling, privacy protection and data access control. Detailed compression rate analysis demonstrates that the proposed solution can effectively reduce the transmission load of image information. Security analysis demonstrates that the proposed approach achieves multi-level reconstruction qualities and high-security strength for users with different authorities. Thus, it has a good application prospect in an image-based intelligent traffic management system.

Intelligent Traffic Management System to Improve Mobility at Ayigya, a Commuter City in Ghana

The issue of vehicular traffic congestion is faced by most road users all over the world, including Ghana. The complications intensify day in and day out, especially in most urban areas, due to development and urbanization. The exponential increase in road users awakens concern for an effective road transportation system to convey people and goods from one place to another. In an attempt to mitigate the effect of the problem, a system based on a statistically programmed lighting sequence was introduced. This technique served its purpose for some time and was realized to be inefficient because it controlled traffic flow by assigning a fixed amount of green light time to each phase of traffic, which meant that green light time was sometimes given to lanes even when there was no conflicting traffic. The persistent nature of the problem requires the need for an intelligent traffic management system to effectively coordinate the flow of vehicles through the available road network. The proposed system works based on priority queuing, where green and red phases are dynamically assigned to lanes depending on the present traffic volume. The proposed system uses two methods of counting to determine the highest lane count. They are the Digital Vehicle Counting (DVC) and the Manuel Vehicle Counting (MVC) methods. An effective detection zone of sixty meters is declared away from the traffic intersection. The values produced by both counting methods are fed to the Traffic Phase Router (TPR) for comparison. The lane with the highest vehicle counts from both counters is given the chance to leave the intersection. The proposed system was designed using Simulation of Urban Mobility (SUMO) software. Results obtained after the simulation showed that the proposed system performed better than the existing system based on the Key Performance Indicators (KPIs) used.

Lightweight Vehicle Detection Based on Improved YOLOv5s.

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach

The challenges of managing traffic in rapidly urbanizing cities with increasing populations. It highlights the need for advanced technologies and frameworks in smart cities to develop intelligent traffic management systems. The proposed approach is a cyber-physical framework that integrates physical infrastructure with digital infrastructure for real-time data collection, analysis, and decision-making. By using sensors, IoT devices, and data analytics, the system captures and processes traffic-related information to predict traffic flow, optimize signal timings, and recommend alternate routes. The system also communicates with connected vehicles to provide real-time updates and alerts, improving driver safety and reducing congestion. The cyber-physical approach allows seamless integration with other smart city systems for efficient coordination and resource allocation. Implementing this system offers benefits such as reduced travel times, improved air quality, enhanced road safety, and increased overall efficiency in urban transportation. It can adapt and evolve by learning from new data inputs and incorporating emerging technologies, ensuring sustainability and scalability. Overall, this paper presents a comprehensive framework for enhancing traffic management in smart cities, transforming urban transportation, and improving residents' quality of life while making cities more sustainable and resilient.

TC-GCN: Triple cross-attention and graph convolutional network for traffic forecasting

With the rapid development of urbanization, increasingly more data are being acquired by intelligent transportation systems (ITSs), which is of great significance for traffic flow forecasting. Efficient intelligent traffic management systems (TMSs) depend on accurately forecasting traffic flows as well as reasonably researching and judging traffic states. However, the current prediction models used in transportation forecasting tasks are traditional temporal- and spatial-dimensional prediction approaches. Especially when faced with road channelization, the numbers of lanes and lane types increase significantly, causing significant increases in the dimensionality and complexity of traffic data, and research models utilizing the cross-interaction relationships among multiple dimensions have not been considered. This has led to unsatisfactory prediction results in complex traffic environments. Therefore, it is very important to explore the exchange–correlation of complex dimensional data and the mining of hidden attributes. This paper presents a method for performing multidimensional cross-attention and spatiotemporal graph convolution. This method fully considers cross-information and constructs an attention cross-view between every pair of dimensions among the channel, time and space domains to model the cross-dimensional dependencies of traffic data. We innovatively propose a triple cross-attention and graph convolutional network (TC-GCN), which can achieve further improved traffic forecasting performance. The TC-GCN is verified on two real-world traffic datasets, namely, METR-LA and PEMS-BAY, and the experimental results are compared with those of multiple advanced baselines, showing that the proposed approach outperforms most of these baselines, which proves the effectiveness of the method proposed in this paper.

ИССЛЕДОВАНИЕ ТРАНСПОРТНОГО ПЛАНИРОВАНИЯ И ПРОЕКТИРОВАНИЯ МАЛЫХ И СРЕДНИХ ГОРОДОВ НА ОСНОВЕ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

With the growth of motorization and the increase in the urban population, the problem of mobility is becoming more and more serious. Transport-related difficulties, such as traffic jams and long travel times, not only affect people's lives, but also have serious economic consequences. Through a comprehensive baseline survey of the current traffic patterns in small and medium towns, we are gaining a more complete and accurate understanding of the spatial and temporal changes and existing traffic problems associated with traffic in small and medium towns. Based on traditional traffic planning methods, combined with an intelligent traffic management system, the intelligent improvement of traffic in small and medium cities in the PRC is proposed, and a road planning improvement plan is proposed to solve the traffic problems existing in small and medium cities and improve efficiency. as a way to solve transport problems.

Revolutionizing Tourism: Harnessing the Power of IoT in Smart Destinations

The convergence of technology and travel has given rise to the paradigm of smart destinations, fueled by the transformative force of the Internet of Things (IoT). This review paper delves into the revolutionary impact of IoT on tourism, specifically focusing on the concept of smart destinations. Smart Hotels, exemplifying this paradigm shift, redefine the guest experience through seamless technology integration. Smart room controls, driven by IoT, intuitively respond to guest preferences, transforming the traditional stay into a personalized and efficient encounter. Transportation undergoes a parallel transformation through connected transportation, where IoT innovations reshape global mobility. From smart airports to intelligent traffic management systems, the integration of IoT ensures seamless and efficient travel experiences for both passengers and goods. Wearable devices further enhance the tourism experience, with smart tour guides leading the way. Wearables provide real-time information and personalized recommendations, with smart glasses employing augmented reality (AR) to overlay digital information onto the physical world. This hands-free approach enriches tourist exploration of historical sites and museums. In popular tourist attractions, IoT-enabled experiences redefine engagement. From smart museums to AR tours and interactive exhibits, the integration of IoT technologies enhances how visitors interact with cultural landmarks, creating immersive and technologically enriched tourism experiences. This review explores the multifaceted intersections of IoT and tourism, offering insights into current trends and future prospects in this dynamic space.

Smart Traffic Management System

With the rapid urbanization and increasing vehicular density in modern cities, the demand for efficient and intelligent traffic management systems has become paramount. This paper presents a comprehensive overview of a Smart Traffic Management System (STMS) designed to optimize traffic flow, enhance safety, and minimize congestion in urban areas. The STMS leverages advanced technologies such as artificial intelligence, sensor networks, and data analytics to create a dynamic and adaptive traffic control infrastructure. The key components of the proposed system include real-time traffic monitoring, predictive analytics, and adaptive signal control. Utilizing a network of sensors, cameras, and other data sources, the STMS continuously gathers information on traffic conditions, identifying patterns and potential issues. This data is then processed through machine learning algorithms to predict traffic trends, enabling proactive management strategies. One of the innovative features of the STMS is its adaptive signal control mechanism. Traditional traffic signal timings are often static and fail to respond promptly to changing conditions. The STMS, however, dynamically adjusts signal timings based on real-time traffic data, optimizing traffic flow and minimizing delays. This adaptability is further enhanced by the integration of vehicle-to-infrastructure (V2I) communication, allowing direct communication between vehicles and the traffic management system. Additionally, the STMS incorporates intelligent decision-making algorithms to prioritize emergency vehicles, public transportation, and other critical services. This ensures a swift and efficient response to emergencies while maintaining overall traffic efficiency. Furthermore, the system includes a user-friendly interface accessible through mobile applications and web platforms. This interface provides real-time traffic updates, alternative route suggestions, and other relevant information to empower commuters with the knowledge needed to make informed travel decisions. The implementation of the Smart Traffic Management System holds the potential to revolutionize urban mobility by significantly reducing travel times, fuel consumption, and environmental impact. This paper discusses the underlying technologies, design considerations, and potential benefits of the STMS, highlighting its role in creating smarter and more sustainable urban transportation networks

A Benchmark Example of Intelligent Traffic Management System using Artificial Intelligence

AbstractThis paper describes a practical methodology for benchmarking the Intelligent Traffic Management System (ITMS) of Smart City Application. It explains the complete development of the application from defining requirements to verifying the software. The system design is demonstrated using MATLAB System Composer. To achieve the intelligent traffic system, we used the You Only Look Once (YOLO) v5 algorithm for vehicle detection. The other deep learning prediction logics are shown to realize the various features such as the optimal route detection, hi‐traffic detection of ITMS. Followed by the verification and structural coverage of Intelligent Traffic Management System is demonstrated with example.

Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization

Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Additionally, a Bluetooth module facilitates communication between the traffic management unit and ambulance vehicles, triggering an alert signal to clear the way for emergencies. The system's adaptability allows for easy integration into existing infrastructure. Results indicate improved traffic efficiency, reduced congestion, and enhanced emergency vehicle response times

Intelligent Traffic Management System Based on Traffic Density

Intelligent Traffic Management Systems (ITMS) play a vital role in efficiently managing traffic congestion in urban areas. This abstract presents an innovative approach to traffic management, specifically focusing on the priority given to emergency vehicles at traffic signals. By integrating advanced technologies such as artificial intelligence and real-time communication systems, this system aims to optimize traffic flow while ensuring the swift passage of emergency vehicles. The proposed ITMS incorporates a dedicated feature that detects and identifies emergency vehicles approaching intersections. This is achieved through a combination of specialized sensors, such as acoustic sensors and GPS- enabled devices, and intelligent algorithms capable of distinguishing emergency vehicle characteristics from regular traffic patterns.

Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors. Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system. Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face. In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption.