Road Traffic Monitoring System Research Articles

Preparation of strong and tough conductive hydrogel based on Grafting, Fe3+-Catechol complexations and salting out for triboelectric nanogenerators

The development of a strong and tough conductive hydrogel capable of meeting the strict requirements of the electrode of a hydrogel-based triboelectric nanogenerator (H-TENG) remains an enormous challenge. Herein, a robust conductive polyvinyl alcohol (PVA) hydrogel is designed via a three-step method: (1) grafting with 3,4-dihydroxy benzaldehyde, (2) metal complexation using ferric chloride (FeCl3) and (3) salting-out using sodium citrate. The hydrogel contains robust crystalline PVA domains and reversible/high-density non-covalent interactions, such as hydrogen bonding, π–π interactions and Fe3+-catechol complexations. Benefiting from the crystalline domains, the hydrogel can resist external forces to the hydrogel network; meanwhile, the reversible/high-density of non-covalent interactions can impart gradual and persistent energy dissipation during deformation. The hydrogel possesses multiple cross-linked networks, with 6.47 MPa tensile stress, 1000 % strain, 35.24 MJ/m3 toughness and 37.59 kJ/m2 fracture energy. Furthermore, the inter-connected porous hydrogel has an ideal structure for ionic-conducing channels. The hydrogel is assembled into an H-TENG, which can generate open circuit voltage of ∼ 150 V, short-circuit current of ∼ 3.0 μA, with superb damage immunity. Subsequently, road traffic monitoring systems are innovatively developed and demonstrated by using the H-TENG. This study provides a novel strategy to prepare superiorly strong and tough hydrogels that can meet the high demand for H-TENGs.

Application of improved you only look once model in road traffic monitoring system

<span lang="EN-US">The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.</span>

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Traffic violation analysis using time series, clustering and panel zero-truncated one-inflated mixed model

Traffic rules violations in urban areas, which can cause traffic crashes and unsafe situations, are a major issue nowadays. The present paper aims to analyze the frequency of traffic violations in Tehran city, Iran, over a five-year period (March 2016- March 2021). The data is obtained via road traffic violation monitoring system which can capture and process various traffic violations. This database, containing about 97 million violations committed by about 16 million drivers, is explored applying three statistical approaches. In the first approach, some multiplicative SARIMA and Bayesian Spatio-temporal models are fitted to the monthly violations. Also, in the second approach, the K-means clustering algorithm is applied to discover homogeneous districts of Tehran Municipality regarding their number of violations and their number of violations per camera towers meter during the study. Finally, in the third approach, a random-effect zero-truncated one-inflated Poisson model is proposed to study factors affecting driver’s number of violations over time.

Performance Evaluation of LTE Random Access Procedure under Distributed Location Data Mining for Road Traffic Monitoring

The widely-used cellular system, such as LTE, has a large potential to serve vehicle-related connectivity which is one of the potential IoT market in the near future. However, the connection-oriented nature of LTE Random Access procedure remains the challenge. The RA procedure in LTE is prone to be overloaded with simultaneous access request from large number of mobile users. This overload condition is likely to happen due to synchronized location reporting for road traffic monitoring in Intelligent Transportation System (ITS). This work studies the performance of RA procedure under overload condition in term of probability of successful report delivery and average report delivery delay. Subsequently, two proposals are made to increase the performance: report time spreading and truncated binary exponential decrease backoff mechanism. The two proposals are evaluated and compared with the existing configuration of LTE RA procedure. Our simulation results show that both proposals can increase the probability of successful report delivery and obtain fairly low average report delivery delay, which is necessary for accurate location reporting and road traffic monitoring system.

A Systematic Review of Drone Based Road Traffic Monitoring System

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.

An improved YOLO-based road traffic monitoring system

The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios.

Investigation of Failures and Vulnerabilities in Road Traffic Air Quality Management System during 2020 Pandemics

Air deterioration and high toxicity affects life quality and longevity. Most important is pollution and toxicity impact upon health and disease management. Air born sicknesses and health problems are a permanent challenge for air dependent life-forms. Lately have been recorded manifestations and a complex phenomenology of air partially transferred pathogenic agents. The air quality is subject to investigation in order to provide a better management of the pollution control system. Pollution is a major contributor to air quality deterioration and to health problem multiplication. Present day pandemic is thought to be partially transmitted through air and by direct contact. Low air quality may be a serious contributing factor to sickness promotion. Air pollution due to road traffic and livestock is one such factor. The main objective of the paper is to investigate some important failures and vulnerabilities recorded in the road traffic air quality monitoring system during 2020 pandemics in our country and in Cluj. Thus, the evolution of chemical composition has been put under the spot of scientific investigation. Failures and weak points have been acknowledged and considered for improvement. At least twelve significant system failures and multiple vulnerabilities have been recorded due to sensor faults and unit malfunction. Social lockdown reduced all pollutants, but some units failed to indicate it properly.

Road Traffic Monitoring System

Nowadays, the Congestion in traffic is one of the major issues in many cities. There are different causes of congestion in road traffic like capacity of the lane is insufficient, more demand, large delays in signals, increase the number of vehicles etc. As populations are growing, it is difficult to manage the traffic on the roads and also the people has to wait for the green signal for a long time as it is not depend on the density of traffic in the real time. This paper presents the method to find the density of the traffics in each signal by using camera and sensors. According to the vehicle density on road, the decisions are made to switch the traffic lights to red or green. As the result it will reduce the traffic on roads which will help to reduce the accidents

A Decision Tree Based Road Recognition Approach Using Roadside Fixed 3D LiDAR Sensors

As one of the most important elements in the intelligent transportation system (ITS), the road traffic monitoring system (RTMS) needs to be functioned with a road recognition mechanism. Current works on road recognition mainly target at the field of automatic driving and cannot be directly used in the RTMS. In this paper, we propose a decision tree-based road recognition algorithm using roadside fixed light detection and ranging (LiDAR) sensors in the RTMS. These LiDAR sensors have a low vertical resolution, which implies that we cannot get a clear far boundary and obvious features of roads from the point cloud data. Point cloud data obtained by the roadside LiDAR sensors are projected onto a plane rasterized to grids of points. Using a decision tree, these grids are first classified into background grids and road grids. For reducing misclassification, these grids are further reclassified using a mean filtering algorithm. Finally, a minimum circumscribed rectangle algorithm is employed to obtain accurate road boundaries. The experiment results show that compared to existing road recognition algorithms, the proposed approach has advantages of being completely automatic, requiring shorter recognition time and having a wider detection range.

Retractation note: Road Traffic Monitoring System with Self-Learning Function using the Raspberry Pi Platform

The authors, Cosmin Rus, Răzvan Marcuş and Olimpiu Stoicuța, regret that this article is an accidental duplication of an article that has already been published: https://www.mathworks.com/matlabcentral/fileexchange/52456-analyzing-trafficusing-a-webcam-a-raspberry-pi-and-thingspeak. The duplicate article has therefore been withdrawn. The Guest Editors have given their approval for this decision.

Retracted Article: Road Traffic Monitoring System with Self-Learning Function using the Raspberry Pi Platform

The authors, Cosmin Rus, Răzvan Marcuş and Olimpiu Stoicuța, regret that this article is an accidental duplication of an article that has already been published: https://www.mathworks.com/matlabcentral/fileexchange/52456-analyzing-trafficusing-a-webcam-a-raspberry-pi-and-thingspeak. The duplicate article has therefore been withdrawn. The Guest Editors have given their approval for this decision.

Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons

The paper proposes a method, which utilizes mobile devices (smartphones) and Bluetooth beacons, to detect passing vehicles and recognize their classes. The traffic monitoring tasks are performed by analyzing strength of radio signal received by mobile devices from beacons that are placed on opposite sides of a road. This approach is suitable for crowd sourcing applications aimed at reducing travel time, congestion, and emissions. Advantages of the introduced method were demonstrated during experimental evaluation in real-traffic conditions. Results of the experimental evaluation confirm that the proposed solution is effective in detecting three classes of vehicles (personal cars, semitrucks, and trucks). Extensive experiments were conducted to test different classification approaches and data aggregation methods. In comparison with state-of-the-art RSSI-based vehicle detection methods, higher accuracy was achieved by introducing a dedicated ensemble of random forest classifiers with majority voting.

A Novel Approach to Road Traffic Monitoring and Control System

A Novel Approach to Road Traffic Monitoring and Control System

Neuromorphic Sensor Network Platform: A Bioinspired Tool to Grow Applications in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) must face the challenge of producing a vast plethora of applications from the least possible number of distributed sensors. In this paper we describe the Neuromorphic Sensor Network (NSN) platform, which implements a bioinspired approach to the development of growing applications in WSNs. NSNs follow an analogy with the neurophysiology of vertebrates, compressing the information coming from sets of sensors in wireless nodes and processing it by means of a set of processing units (PUs) that perform individual general purpose functions. Applications are then constructed through specific connections of these PUs, generating application pathways that allow the reuse of the same distributed NSN to give response to any desired output, thus achieving an application scalability. We illustrate the detailed process of growing applications using the NSN platform through an object tracking tool that mimics the behavior of the vertebrates visual system to detect and track objects. Finally, we describe a real implementation of the NSN platform in a road traffic monitoring and information system currently in operation in the cities of Madrid and Seville (Spain).

Wireless Sensor Networks for Road Traffic Monitoring

Wireless Sensor Networks (WSNs) consist of large number of sensor nodes. Each node is empowered by a com-munication interface that is mainly characterized by low power, short transmission distance and minimal data rate such as the maximum data rate in ZigBee technology is 256 kbps, while approximately the physical transmission range between 10 to 20 meters. Currently, WSN Technology is being distributed over a large roadway of areas, in order to monitor traffic and environmental data. This approach allows several Intelligent Transport Systems (ITSs) applications to exploit the primary collected data in order to generate intelligent decisions based on earlier valuable selected information. Therefore, in this work we present a MAC protocol that is suitable for WSN where its nodes are assigned to a linear topology. The investigated protocol is realized by adapting an already existing Jennic MAC protocol. We demonstrate the validity of the MAC by building a complete end-to-end road traffic monitoring system using 4 Jennic nodes deployed in an indoor environment with the aim to prove the MAC potential in meeting the expectations of ITS applications. It is appropriate to mention that the proposed implementation just considers stationary WSN nodes.

Electronic Toll and Road Traffic Monitoring System Using 3-D Field AMR Sensors

Electronic Toll and Road Traffic Monitoring System Using 3-D Field AMR Sensors