Real-time Traffic Monitoring System Research Articles

IoT-Driven solutions for VANET trustworthiness: Examining misconduct and position security challenges

Vehicular ad hoc networks (VANETs) improve the traffic management and road safety by exchanging real-time data between automobiles and roadside infrastructure. Reliable Intelligent Transport Systems (ITS) solutions tend to boost VANET data security, integrity and dependability. These systems communicate VANET data between infrastructure and automobiles using sensors, Internet of Things (IoT) devices and communication technologies. IoT-driven includes a Real-Time Traffic Observation optimizes VANET route design. Traffic management and safety improve with ITS-connected cars and infrastructure. Remote communication technologies enhance traffic management and decision-making by sending a real-time data between cars and infrastructure. Smart parking systems use real-time parking availability data from IoT sensors and devices to maximize space use and reduce congestion. Security and misbehavior are dependable problems in 40 IoT-driven VANET studies. Miscommunication and unstandardized are the main security issues which pose hazards. This review shows VANETs that requires authentication, encryption and anomaly detection to prevent attacks and IoT for transportation sector. This extensive review covers VANET reliability and IoT-driven real-time traffic monitoring, ITS, remote communication and intelligent parking systems. This review analyzes the metrics to assess the many detection techniques which includes, misconduct-Aware On-Demand Collaborative Intrusion Detection System (MACIDS) and Collision-Induced Dissociation Spectroscopy (CIDS), (i) Accuracy (ii) False Positive Rate, (iii) precision, (iv) recall (v) False Negative Rate and (vi) F1-Score in VANET security in Attack Detection Rates (ADR), misbehavior detection, evaluation metrics and detection algorithms were studied using CIC-IDS data. Due to misconduct and security issues, a strong IoT-driven solutions are required to improve VANET trustworthiness and offers a secure and reliable vehicular communication networks for safer and more efficient transportation systems.

Integrating machine learning techniques with IoT sensors and connected vehicles to enable real-time traffic monitoring and Adaptive signal control systems

This research investigates machine learning algorithms to traffic monitoring and adaptive signal control coordinated in the context of the Internet of things sensors and connected vehicles for real-time traffic supervision. It measures the efficiency of machine learning algorithms, simulates adaptive control strategies for signal systems, and addresses the challenges of deploying signal system changes. The performance metrics suggest better traffic flow, leading to a decrease in traffic congestion. Further study should be made towards parametrization of algorithms, improvement of communication standards and training of integration models.

Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques

The increasing number of vehicles on the road has made traffic regulations challenging to manage, particularly in large and crowded cities. Real-time traffic monitoring systems are one of the most important factors that enable efficient traffic flow and enhanced mobility. Therefore, vehicles and drivers have always needed reliable and accurate real-time traffic information. Recently, various solutions have been proposed to solve the problems and concerns in traffic situations. One alternative solution is vehicular cloud computing (VCC). Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. The main contributions of this research work are to implement two deep learning techniques based on modified LeNet-5 for real-time traffic sign recognition and the transfer learning-based Inception-V3 model for detecting and recognizing traffic lights. Furthermore, optimal distance was found between the ultrasonic sensors and the obstacles using ultrasonics’ waves time and speed to reduce road accidents. The data, which is collected by sensors and cameras, is processed using various image processing algorithms and it is sent to the cloud to be available for drivers and commuters through a mobile application. Test results indicate that the proposed models have significant improvements in terms of accuracy. The modified LeNet-5 achieved accuracy rates of 99.12% and 99.78% on the German Traffic Sign Recognition Benchmark (GTSRB) and extended GTSRB (EGTSRB) datasets, respectively, whereas the second model, trained on Laboratory for the Intelligent and Safe Automobiles (LISA) dataset, attained a 98.6% accuracy rate. Compared to the related traffic monitoring systems, the findings of this study outperform other works by 3.78% for traffic sign recognition and by 1.02% for traffic light detection and recognition.

Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8

Computer vision applications are important nowadays because they provide solutions to critical problems that relate to traffic in a cost-effective manner to reduce accidents and preserve lives. This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art you-only-look-once v8 algorithm, benefiting from its functionalities to provide vehicle detection, classification, and segmentation. The proposed work provides various important traffic information, including vehicle counting, classification, speed estimation, and size estimation. This information helps enforce traffic laws. The proposed system consists of five stages: The preprocessing stage, which includes camera calibration, ROI calculation, and preparing the source video input; the vehicle detection stage, which uses the convolutional neural network model to localize vehicles in the video frames; the tracking stage, which uses the ByteTrack algorithm to track the detected vehicles; the speed estimation stage, which estimates the speed for the tracked vehicles; and the size estimation stage, which estimates the vehicle size. The results of the proposed system running on the Nvidia GTX 1070 GPU show that the detection and tracking stages have an average accuracy of 96.58% with an average error of 3.42%, the vehicle counting stage has an average accuracy of 97.54% with a 2.46% average error, the speed estimation stage has an average accuracy of 96.75% with a 3.25% average error, and the size estimation stage has an average accuracy of 87.28% with a 12.72% average error.

IoT based real-time traffic monitoring system using images sensors by sparse deep learning algorithm

Intelligent traffic monitoring systems are necessary and useful tools due to the emerging technologies related to the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of both technologies can facilitate better urban traffic management for fast networks, such as 5G, 5G+ and 6G environments. However, existing studies focus on complex and expensive solutions or present a latency for generating new training models with accuracy not superior to 98%. Thus, this paper proposes a vehicle identification using a sparse and soft variant of the CNN-based approach, the LightSpaN, which offers a fast training model, without using a complex solution. The effectiveness of the proposed solution is evaluated using the Simulation of Urban MObility (SUMO) tool and a real vehicle traffic implementation using IoT devices. The proposal identified the majority kind of vehicles in a short period of time, faster and more accurately than the related works. The results validated the proposed solution for a real-time traffic monitoring system, presenting an average accuracy of around 99.9% for emergency vehicles. Furthermore, a reduction of both total waiting time and total traveling time was reached by our proposal.

A vision-based real-time traffic flow monitoring system for road intersections.

In this study, a vision based real-time traffic flow monitoring system has been developed to extract statistics passes through the intersections. A novel object tracking and data association algorithms have been developed using the bounding-box properties to estimate the vehicle trajectories. Then, rich traffic flow information such as directional and total counting, instantaneous and average speed of vehicles are calculated from the predicted trajectories. During the study, various parameters that affect the accuracy of vision based systems are examined such as camera locations and angles that may cause occlusion or illusion problems. In the last part, sample video streams are processed using both Kalman filter and new centroid-based algorithm for comparative study. The results show that the new algorithm performs 9.18% better than Kalman filter approach in general.

Applications of fiber optic sensors in traffic monitoring: a review

Instrumenting pavement with fiber optic sensors has recently gained popularity as a part of the digital infrastructure transformation. In this survey, we present some of the recent real-world implementations of fiber optic sensors as real-time traffic monitoring systems. We highlight advantages and limitations of the surveyed instrumentations. We identify preferred range of values for some of the key design parameters that are necessary for durability and accurate weighing of individual axles. These parameters include strain resolution, scanning rate, installation depth, spatial resolution, and sensor orientation. Some of the studies report excellent results in terms of vehicle axle count, weight, and speed achieving accuracy levels above 90% with single digit margin of error. Lastly, we draw attention to some of the outstanding challenges from technical, operational, and scalability points of views.

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.

Development of a Smart Traffic Light Control System Using IoT Technology

The development of a smart traffic light control system using IoT technology has become an essential topic in urban traffic management. This research paper focuses on the development of an IoT-based real-time traffic monitoring system for city governance. The system integrates traffic data from various sources, including cameras, sensors, and other data sources, to provide real-time traffic updates to city officials and drivers. The proposed system utilizes IoT technology to collect data from various traffic sources and processes it to determine the optimal time for traffic lights to switch between green, yellow, and red. The system uses a combination of machine learning algorithms and traffic models to predict traffic flow patterns and optimize traffic light timings. Additionally, the system can identify potential traffic accidents and send alerts to nearby emergency services. The proposed system is evaluated using simulation techniques and has shown a significant improvement in reducing traffic congestion and improving traffic flow in a simulated urban environment. Furthermore, the system provides real-time data analytics to city officials, allowing them to make informed decisions on traffic management and city planning. Overall, the development of a smart traffic light control system using IoT technology has significant potential in improving traffic management in urban areas. The proposed system can reduce traffic congestion, improve traffic flow, and enhance overall traffic safety. Therefore, it is essential to continue research and development in this area to implement the proposed system on a larger scale.

Improved Accuracy of Vehicle Counter for Real-Time Traffic Monitoring System

Abstract This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.

Development of an IoT based real-time traffic monitoring system for city governance

A significant amount of research work carried out on traffic management systems, but intelligent traffic monitoring is still an active research topic due to the emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of these technologies will facilitate the techniques for better decision making and achieve urban growth. However, the existing traffic prediction methods mostly dedicated to highway and urban traffic management, and limited studies focused on collector roads and closed campuses. Besides, reaching out to the public, and establishing active connections to assist them in decision-making is challenging when the users are not equipped with any smart devices. This research proposes an IoT based system model to collect, process, and store real-time traffic data for such a scenario. The objective is to provide real-time traffic updates on traffic congestion and unusual traffic incidents through roadside message units and thereby improve mobility. These early-warning messages will help citizens to save their time, especially during peak hours. Also, the system broadcasts the traffic updates from the administrative authorities. A prototype is implemented to evaluate the feasibility of the model, and the results of the experiments show good accuracy in vehicle detection and a low relative error in road occupancy estimation. The study is part of the Omani-funded research project, investigating Real-Time Feedback for Adaptive Traffic Signals.

Design of Traffic Monitoring System Based on Video Detection

Traffic monitoring system is an important means to ensure the safe operation of traffic. According to the characteristics of road traffic monitoring environment, a real-time traffic monitoring system is designed by using image processing, computer vision, pattern recognition and other technologies. Experiments show that the system can effectively detect various typical traffic incidents and traffic operation parameters, and provide a favorable guarantee for traffic safety.

Real-time visual detection and tracking system for traffic monitoring

Computer vision systems for traffic monitoring represent an essential tool for a broad range of traffic surveillance applications. Two of the most noteworthy challenges for these systems are the real-time operation with hundreds of vehicles and the total occlusions which hinder the tracking of the vehicles. In this paper, we present a traffic monitoring approach that deals with these two challenges based on three modules: detection, tracking and data association. First, vehicles are identified through a deep learning based detector. Second, tracking is performed with a combination of a Discriminative Correlation Filter and a Kalman Filter. This permits to estimate the tracking error in order to make tracking more robust and reliable. Finally, the data association through the Hungarian algorithm combines the information of the previous steps. The contributions are: (i) a real-time traffic monitoring system robust to occlusions that can process more than four hundred vehicles simultaneously; and (ii) the application of the system to anomaly detection in traffic and roundabout input/output analysis. The system has been evaluated with more than two thousand vehicles in real-life videos.

Evaluation and augmentation of traffic data including Bluetooth detection system on arterials

Recently, traffic agencies have implemented Bluetooth detection systems (BDS) on arterials to collect traffic data and purchased data from private sector companies such as HERE, INRIX, and TomTom for real-time traffic monitoring and long-term traffic system management and performance evaluation. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. This study utilized high-resolution GPS trajectory to evaluate data from private sector, HERE and BDS of a principal arterial corridor in Orlando, FL, USA. The results showed that the accuracy and reliability of BDS data are better than private sector data on the study corridor, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In order to improve the quality of private sector data, information about bimodal traffic flow was extracted by a finite mixture model from historical BDS data and incorporated with the real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the corridor except for segments highly influenced by traffic from or to the expressway ramps where evaluation datasets might have some bias.

Semantic Interoperable Traffic Management Framework for IoT Smart City Applications

Real-time traffic monitoring and controlling are one of the biggest problems in this present living world. So many researchers have dealt with and put their effort into this problem, as a result, several types of approaches have developed. Currently using traffic monitoring and alert systems are not up to the needs of smart city applications and more expensive. This paper proposes an Internet of Things (IoT) based Smart Real-Time Traffic Monitoring System to provide better service with low cost for Smart city applications using semantic annotations. The proposed framework contains two phases namely-traffic monitoring unit and traffic reduction unit. The first phase analyses semantic traffic to detect an emergency, the latter phase removes redundant semantic information for traffic reduction. Simulation results suggest that the framework is capable of accurate and early detection of an emergency as well as traffic reduction while keeping sufficient information to report the emergency.

A multi-mode real-time system verification model using efficient event-driven dataset

Real-time systems experience many safety and performance issues at run time due to different uncertainties in the environment. Systems are now becoming highly interactive and must be able to execute in a changing environment without experiencing any failure. A real-time system can have multiple modes of operation such as safety and performance. The system can satisfy its safety and performance requirements by switching between the modes at run time. It is essential for the designers to ensure that a multi-mode real-time system operates in the expected mode at run time. In this paper, we present a verification model that identifies the expected mode at run time and checks whether the multi-mode real-time system is operating in the correct mode or not. To determine the expected mode, we present a monitoring module that checks the environment of the system, identifies different real-world occurrences as events, determines their properties and creates an event-driven dataset for failure analysis. The dataset consumes less memory in comparison to the raw input data obtained from the monitored environment. The event-driven dataset also facilitates onboard decision-making because the dataset allows the system to perform a safety analysis by determining the probability of failure in each environmental situations. We use the probability of failure of the system to determine the safety mode in different environmental situations. To demonstrate the applicability of our proposed scheme, we design and implement a real-time traffic monitoring system that has two modes: safety, and performance. The experimental analysis of our work shows that the verification model can identify the expected operating mode at run time based on the safety (probability of failure) and performance (usage) requirements of the system as well as allows the system to operate in performance mode (in 3295 out of 3421 time intervals) and safety mode (in 126 out of 3421 time intervals). The experimental result demonstrates that the volume of the dataset generated using our approach is significantly less in comparison to existing data compression techniques.

Real-Time Bus Video Monitoring System Based on 3G Network

According to some bottleneck problems of the communication network bandwidth in wireless video transmission, a design scheme of real-time traffic video monitoring system based on 3G network is put forward in this paper. The design of hardware and the software realization of the system process are mainly introduced. TMS320DM8168 is selected to build the hardware platform in this system. H.264 video encoder is integrated internally. The real-time transmission of video data is sent to the remote monitoring center through the 3G network to improve the video transmission quality. The system meets the requirements of video transmission applied in automobiles.

A clustering approach to online freeway traffic state identification using ITS data

Online traffic state identification plays an important role in achieving the potentials promised by Intelligent Transportation Systems (ITS) on various traffic applications, e.g., real time traffic monitoring systems. Traditional approaches have shown the limitations in either obtaining the necessary pre-determined information or having difficulties in their online implementation. This paper introduces an online agglomerative clustering procedure for freeway traffic state identification using ITS data, represented by three traffic condition variables of flow rate, speed, and occupancy. An optimal fit of the statistical characteristics is provided by maximizing the intra-cluster data point distances and minimizing inter-cluster data point distances through a joint utilization of the Bayesian Information Criterion and the ratio of change of the dispersion measurements. Test results show that the identified freeway traffic states through the proposed procedure are reasonable and consistent with the common understandings on freeway traffic operating conditions.