Intelligent Traffic Management Research Articles

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

Smart Vehicle Parking Facility and Monitoring System Using ML

In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System designed to address the challenges of parking management without the need for sensors or IoT technology. This system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System implemented as a web application using HTML, CSS, and JavaScript, without the need for sensors or IoT technology. This web-based system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.

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.

TRAFFIC LIGHT DETECTION WITH YOLO MODELS

The accurate detection and recognition of traffic lights are paramount in the realm of autonomous driving systems and intelligent traffic management. This study leverages the comprehensive cinTA_v2 Image Dataset on Robotflow, specifically designed for traffic light detection, to evaluate the performance of advanced You Only Look Once (YOLO) models, including YOLOv7l, YOLOv8n, YOLOv8s, and YOLOv8m. Through meticulous training and evaluation, we systematically analyze the models' ability to accurately detect and classify traffic light states (green, red, and yellow) under a variety of challenging conditions. Our findings reveal significant improvements in precision, recall, and mean Average Precision (mAP) across the models, with YOLOv8m demonstrating superior overall performance, especially in terms of mAP50-95, reflecting its enhanced capability in detecting small and partially obscured traffic lights. The study not only showcases the effectiveness of YOLO models in a critical application within the autonomous driving domain but also highlights the potential for further advancements in traffic light detection technologies. By discussing the challenges, limitations, and future directions, this work contributes to the ongoing efforts to improve road safety and efficiency through the application of cutting-edge artificial intelligence techniques.

Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism

Precise and rapid detection of seatbelts is an essential research field for intelligent traffic management. In order to improve the detection precision of seatbelts and speed up algorithm inference velocity, a lightweight seatbelt detection algorithm is proposed. Firstly, by adding the G-ELAN module designed in this paper to the YOLOv7-tiny network, the optimization of construction and reduction of parameters are accomplished, and the ResNet is compressed with the channel pruning approach to decrease computational overheads. Then, the Mish activation function is utilized to replace the Leaky Relu in the neck to enhance the non-linear competence of the network. Finally, the triplet attention module is integrated into the model after pruning to make up for the underlying performance reduction caused by the previous stage and upgrade overall detection precision. The experimental results based on the self-built seatbelt dataset showed that, compared to the initial network, the Mean Average Precision (mAP) achieved by the proposed GM-YOLOv7 was improved by 3.8%, while the volume and the computation amount were lowered by 20% and 24.6%, respectively. Compared with YOLOv3, YOLOX, and YOLOv5, the mAP of GM-YOLOv7 increased by 22.4%, 4.6%, and 4.2%, respectively, and the number of computational operations decreased by 25%, 63%, and 38%, respectively. In addition, the accuracy of the improved RST-Net increased to 98.25%, while the parameter value was reduced by 48% compared to the basic model, effectively improving the detection performance and realizing a lightweight structure.

Real-Time Video Analysis for Intelligent Traffic Management, Public Safety, and Security: A Deep Learning Approach

This paper proposes a novel centralized traffic management system designed to enhance road safety and streamline law enforcement. The system leverages a network of intelligent sensors and cameras to achieve real-time accident detection, traffic violation identification, and facial recognition. Data collected from these sensors is fed into a centralized hub for processing and analysis. Upon identifying an incident, the system promptly dispatches notifications to patrolling units, enabling faster response times for accidents and violations. Additionally, facial recognition capabilities allow for real-time identification of individuals, potentially aiding in apprehending criminals or locating missing persons. This paper will discuss the system architecture, core functionalities, and potential benefits for improved traffic management and public safety. We will also address the challenges associated with data privacy and security in such a centralized system.

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.

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

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.

CrackTinyNet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection

AbstractWith the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

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.