Real-time Traffic Monitoring Research Articles

Traffic Flow Outlier Detection for Smart Mobility Using Gaussian Process Regression Assisted Stochastic Differential Equations

Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observations. The proposed method utilizes Stochastic Differential Equations (SDEs) and Gaussian Process Regression (GPR). By employing SDEs, we can capture drift and diffusion estimates in traffic streaming data, providing a more comprehensive modeling of the data generation process. Integrating GPR allows precise Bayesian posterior inferences for outlier detection within the SDE framework. To improve practicality, we introduce a flexible threshold-setting mechanism using statistical testing to control the false positive rate. This adaptability helps strike a balance between model fitting and complexity in outlier detection. Compared to traditional SDE-based methods, our SDE-GPR outlier detection method demonstrates enhanced robustness and better adaptability to the complexities of traffic systems. This is evidenced through an empirical study using time series data collected in California, USA. Overall, this study introduces a more advanced and accurate approach to outlier detection in traffic streaming data, paving the way for improved real-time traffic condition monitoring and management.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.

A Comprehensive Machine Learning Approach for Advanced Vehicle Detection and Counting

The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a computational model for intelligent vehicle detection and tracking at a given location and time of day, using real-time images of passing cars. This approach uses OpenCV to evaluate the model's car detection and counting capabilities, and convolutional neural networks (CNNs) for object recognition and classification. The techniques laid the groundwork for early improvements, which were often enhanced using machine learning classifiers such as random forests and support vector machines (SVMs). To automate the process and get useful information regarding traffic patterns and management.

Vehicle Classification and Counting for Traffic Analysis based on Single-stage YOLOv8 Model

Intelligent traffic systems are emerging and becoming part of smart city infrastructure that requires computer vision applications to provide traffic information like vehicle classification and counting in real-time. Vehicle counting helps detect heavy traffic on roads, and vehicle classification helps enforce further processes like speed estimation to enforce speed limit laws based on the vehicle class. Deep learning computer vision-based systems provide automatic feature extractions that are robust to changes in lighting, shadows, and occlusions. This paper proposes a software solution for a real-time traffic monitoring system based on a cutting-edge single-stage deep learning model through the state-of-the-art YOLOv8 algorithm. YOLOv8 is the most recent model of the YOLO family, which provides object detection and classification through its CNN architecture. The proposed work detects vehicles and counts them based on their class. The four common vehicle classes are sedan cars, buses, trucks, and motorcycles, and a counter for each class is displayed on the system’s output screen in real-time and recorded in a log file. The results of the proposed system running on the Nvidia GTX 1070 GPU show an average accuracy of 96.58% with an average error of 3.42% for vehicle detection and an average accuracy of 97.54% with a 2.46% average error for vehicle counting. For vehicle classification, the results for the four vehicle classes (car, bus, truck, and motorcycle) show an accuracy of (94.7%, 94.7%, 96.2%, 99.7%), precision (95%, 100%, 81.4%, 100%), recall (97.9%, 36.3%, 100%, 66.6%), and the f1-score (96.3%, 53.2%, 89.7%, 79.9%), respectively. Index Terms— Computer Vision, Deep Learning, Vehicle Detection, Traffic Analysis, YOLO.

A Careful Examination of the Security and Traffic Effects Associated with the Vehicle Ad-hoc Network (VANET)

During recent years, vehicular ad hoc networks, or VANETs, have emerged as one of the most exciting and difficult study fields. VANET is regarded as a subset of MANET, or mobile ad hoc network, which is primarily used on automobiles. With the use of traffic data, VANET hopes to supply an advanced Intelligent Transportation System (ITS) with a wealth of information. Mention how daily traffic data collection helps with transportation planning, particularly with regard to intra-city communication. Two of the main issues with the existing traffic system are jams in traffic and accidents. Around the world, numerous people lose their lives or suffer severe injuries in traffic accidents each year. Human lives on the road are directly impacted by these issues. VANET can help to avert these mishaps and facilitate the efficient operation of daily traffic. Furthermore, VANET offers a plethora of intriguing applications, including real-time traffic condition monitoring, dynamic route scheduling, blind crossing prevention, safety, and more. In addition, there are certain drawbacks in terms of traffic and security. The absence of a centralised infrastructure in VANET is one of the main security issues. One of the most difficult jobs in the resultant decentralised and self- organizing VANETs is the administration of the wireless channel to make an efficient use of its capacity because there is no centralised infrastructure in charge of synchronisation and coordination of transmissions. VANET is primarily used to lower the risk of accidents in urban areas with a high volume and complexity of vehicles. We introduce the VANET Security R&D Ecosystem and examine traffic in this article. The four main components of the R&D ecosystem are end customers, government agencies, automakers, and university research. Every facet of VANET is covered in detail. Our study primarily focuses on the security and traffic within VANETs, including how data is safeguarded from vehicle-to-vehicle (V2V), vehicle-to-roadside infrastructure communications devices (V2I), access points, and other sources.

Enabling traffic forecasting with cloud-native SDN controller in transport networks

Network bandwidth is a scarce resource that network operators monitor to cope with future traffic demands and plan more transceiver and fibre deployments. The inclusion of Machine Learning permits the usage of traffic forecasting methods to predict future link usage. Typically, traffic analysis is performed offline due to the high computational load and difficulty of obtaining real-time data directly from the underlying network devices. To overcome these limitations, this paper presents and evaluates an architecture for SDN-controlled packetoptical transport networks to allow real-time traffic monitoring in the transport SDN controller. The presented SDN controller is based on a micro-service-based architecture, which facilitates the ease of deployment of the proposed solution. Four forecasting methods are proposed and evaluated against two topologies to select the most precise and the fastest among them.The algorithm random forest seems to be the most accurate forecasting future link usage with 79.98 % and 95.88 % accuracy and a reasonable fast speed when implemented it into two different topologies

Live Intersection Data Acquisition for Traffic Simulators (LIDATS).

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

Assessment of Its Implementation on Safety on Yamuna Expressway

With the promise of fast connectivity between Greater Noida and Agra, the Yamuna Expressway represents modern infrastructure in India. Amidst all this splendor, though, is a grave worry: security. In order to reduce hazards and improve safety standards, this article investigates the complex network of obstacles that has been encountered while implementing safety measures on the highway. Numerous accidents, including fatalities, have been a recurring problem on the Yamuna Expressway since its opening. The expressway's relatively straight and smooth construction makes it easy for drivers to speed and act irresponsibly, which is the main cause of these incidents. Drivers disregard safety protocols and speed limits with complete disregard for the consequences, which is worsened by the lack of effective enforcement of traffic regulations. The absence of necessary safety elements is one of the primary obstacles to guaranteeing safety on the Yamuna Expressway. Poor lighting, inadequate signage, and restricted emergency services are some of the initial shortcomings that have greatly impacted drivers' ability to navigate safely, especially in bad weather or at night. There have been ongoing attempts to resolve safety problems, but the results have been mixed. The expressway's safety infrastructure has been strengthened by the implementation of measures such as crash barriers, better signage, expanded CCTV surveillance, and more police patrolling. Unfortunately, problems with corruption within law enforcement agencies, a lack of staff, and limited resources make it difficult to apply these restrictions effectively. The Yamuna Expressway can only be made safer with a comprehensive plan. A culture of safety among drivers can be greatly enhanced by public awareness programs that promote responsible driving and emphasize the repercussions of reckless driving. Intelligent transportation systems (ITS) integrate cutting-edge tech to improve traffic management and accident prevention via tools like speed cameras, automatic toll collecting, and real-time traffic monitoring. The Yamuna Expressway is an impressive example of India's infrastructure, yet it will remain unfulfilled until the critical problem of safety is resolved. The Yamuna Expressway has the potential to become a model of safe and efficient transportation for the millions of people who use it every day if we recognize the problems, find lasting solutions, and encourage everyone to do their part to keep everyone safe.

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.

A Review on Highway Network System - Samruddhi Expressway

Highway networks serve as critical arteries of transportation infrastructure, facilitating the movement of goods and people across vast geographical regions. As traffic volumes continue to increase and technological advancements reshape transportation systems, there is a pressing need to optimize the efficiency, safety, and sustainability of highway networks. This abstract presents a comprehensive system approach aimed at addressing these challenges. The proposed highway network system integrates advanced technologies such as intelligent transportation systems (ITS), real-time traffic monitoring, data analytics, and machine learning algorithms to optimize traffic flow, minimize congestion, and enhance safety. By leveraging data from various sources including sensors, cameras, and connected vehicles, the system enables proactive traffic management and congestion prediction, allowing authorities to implement timely interventions and mitigate potential disruptions. Furthermore, the integration of artificial intelligence and machine learning algorithms enables predictive maintenance of infrastructure, identifying potential issues before they escalate into critical failures. This proactive approach not only reduces maintenance costs but also enhances the reliability and lifespan of highway assets. Moreover, the system prioritizes safety through the implementation of automated collision avoidance systems, adaptive speed control, and real-time incident management. By leveraging vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication technologies, the system enables vehicles to exchange critical information, such as hazardous road conditions or impending collisions, thereby reducing the risk of accidents and improving overall safety. Additionally, the system incorporates sustainability principles by optimizing traffic flow patterns to minimize fuel consumption and emissions, promoting eco-friendly transportation practices, and facilitating the integration of electric and autonomous vehicles into the existing infrastructure.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

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.

An Optimized Dynamic Traffic Control System using OpenCV

Abstract: This study delves into a web-based platform designed to apprehend speeding vehicles. It harnesses the capabilities of OpenCV, a robust computer vision library, to accurately detect high-speed cars. Furthermore, the system integrates a specialized model for identifying vehicles deviating from their designated lanes, thereby fostering safer road conditions. To streamline the process of vehicle identification and potential ticket issuance, it incorporates automatic license plate recognition through Azure Cognitive Services. By amalgamating these advanced technologies, the proposed system offers a comprehensive solution for realtime traffic monitoring and regulation enforcement, with the overarching goal of enhancing road safety and traffic management.

Triboelectric nanogenerator for self-powered traffic monitoring

This work aims to create energy-harvesting composites using poly(ethylene-co-vinyl acetate) EVA, lead zirconate titanate (PZT) and polyaniline (PANI) composites. The novelty lies in the fabrication of a triboelectric nanogenerator (TENG) using EVA-PZT-PANI composites. These composites combine the flexibility of EVA polymer with the electricity-generating power of PZT ceramic and the conductivity of PANI. Systematic investigation of the structural, electrical, and microstructural properties of these composites is carried out to optimize their energy-harvesting performance. Among various compositions, the EVP3 device demonstrates exceptional output, generating a peak voltage of 17.8 V and a current of 190 nA at 2 Hz and 5 N applied force. The TENG demonstrates its versatility in harvesting energy from various sources by powering capacitors and collecting energy from human motions. Moreover, we show the potential application of the TENG for real-time traffic monitoring. By embedding the TENG into roads, we can measure car speeds and serve as a traffic management counter, opening up new possibilities for self-powered intelligent transportation systems.

AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability

AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability

Advanced Drone Surveillance System for Traffic Management

The rapid urbanization and increasing vehicle traffic in metropolitan areas have given rise to a significant challenges in traffic management. Traditional methods of a traffic monitoring and control often struggle to cope with the dynamic and complex nature of urban traffic patterns. In this context, we propose an innovative Drone Surveillance System for Traffic Management to revolutionize the way traffic is monitored, analyzed, and optimized in urban environments. Our system leverages state-of-the-art drone technology equipped with advanced sensors, high-resolution cameras, and intelligent algorithms to provide real-time traffic insights. Drones are deployed strategically across key traffic arteries, allowing for comprehensive aerial coverage and data collection. The system focuses on several key objectives: real-time traffic monitoring, incident detection, traffic flow analysis, optimized traffic signal control, emergency response support, data-driven decision making, and ensuring privacy and security compliance. By the employing cutting-edge computer vision algorithms, machine learning techniques, and data analytics, the Drone Surveillance System processes vast amounts of traffic data with speed and precision. The system detects the traffic incidents and the such as accidents and road hazards promptly, enabling swift a responses from emergency services. It analyzes traffic patterns and predicts congestion, allowing for adaptive traffic signal control and dynamic route optimization, thereby minimizing congestion and reducing travel time for a commuters. One of the system’s notable features is its a ability to the support emergency response teams effectively. Drones identify incidents and relay on the critical information to emergency services, International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 02 | February - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28534 | Page 2 enabling rapid deployment and efficient management of emergencies. Moreover, the system ensures the privacy of individuals through strict adherence to regulations and deploys robust security measures to safeguard data integrity. An important advancement in the urban traffic control system is the Drone Surveillance System for Traffic control. The technology turns traditional traffic management into a proactive, flexible, and data-driven procedure by utilizing the power of drones, real-time data processing, and sophisticated algorithms. This innovation not only makes urban transportation more efficient, but it also helps to improve road safety by lowering accident rates, easing traffic, and creating a more sustainable urban environment. Keywords: Traffic Management, Real-time Monitoring, Incident Detection, Traffic Flow Analysis, Optimized Traffic Signal Control, Machine Learning Techniques. Drone Surveillance System

Reducing Traffic Congestion Using Real-Time Traffic Monitoring with YOLOv8

Reducing Traffic Congestion Using Real-Time Traffic Monitoring with YOLOv8

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.