scholarly journals Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2679
Author(s):  
Zhoujing Ye ◽  
Guannan Yan ◽  
Ya Wei ◽  
Bin Zhou ◽  
Ning Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Monitoring System ◽  
Information Acquisition ◽  
Vibration Signal ◽  
High Energy ◽  
Traffic Monitoring ◽  
Vibration Monitoring ◽  
Traffic Information ◽  
Real Time Traffic

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.

An Application of Emission Monitoring System Based on Real-time Traffic Monitoring

2013 ◽  
Vol 4 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Jaesun Park ◽  
Sang Boem Lim ◽  
KiHo Hong ◽  
Mu Wook Pyeon ◽  
Jin You Lin
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Monitoring ◽  
Real Time Traffic ◽  
Emission Monitoring

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Hong-Wei Lioa
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Video Data ◽  
Data Sets ◽  
Speed Estimation ◽  
Detection Zone ◽  
Real Time Traffic

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%.

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

2020 ◽  
Vol 2 ◽  
pp. 230-245
Author(s):  
Mohammed Sarrab ◽  
Supriya Pulparambil ◽  
Medhat Awadalla
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Monitoring ◽  
Real Time Traffic ◽  
City Governance

scholarly journals Artificial Intelligence Enabled Traffic Monitoring System

2020 ◽  
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Real Time Traffic ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

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