Monitoring Traffic Information with a Developed Acceleration Sensing Node

Ye, Zhoujing; Wang, Linbing; Xu, Wen; Gao, Zhifei; Yan, Guannan

doi:10.3390/s17122817

Cited by 24 publications

(12 citation statements)

References 30 publications

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“…The pavement vibration data at each node with zero as the reference value can be obtained by subtracting the raw vibration data from the obtained reference value. Subsequently, filtering and smoothing processing is performed on raw pavement vibration data to extract the peak and corresponding time information [ 36 ].…”

Section: Cloud Platformmentioning

confidence: 99%

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

Yan

Wei

et al. 2021

Sensors

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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.

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Section: Cloud Platformmentioning

confidence: 99%

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

Yan

Wei

et al. 2021

Sensors

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“…Many intelligent transportation systems need a robust and fast sensor for detecting and tracking multiple vehicles. Some examples are vehicle speed estimation solutions [1,2], illegal parking detection modules [3], traffic estate estimation systems [4,5,6] or vehicle detection in aerial images for surveillance [7,8].…”

Section: Introductionmentioning

confidence: 99%

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor

López-Sastre

Herranz-Perdiguero

Guerrero-Gómez-Olmedo³

et al. 2019

Sensors

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In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.

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“…Due to the substantial improvement of acceleration sensing technologies, the microelectromechanical system (MEMS)-based acceleration sensors have been used in the monitoring of pavement vibration. e acceleration signals caused by the moving vehicle load were processed to obtain traffic information including vehicle speed, axle, weight, and traffic volume [2][3][4][5]. Moreover, by detecting the acceleration of pavement under specific wheel loadings and analyzing the vibration modes, it is possible to evaluate the pavement service condition.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Pavement Vibration Response for Roadway Service Condition Evaluation

Wang

2018

Advances in Civil Engineering

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Dynamic response of pavement provides service condition information and helps with damage prediction, while limited research is available with the simulation of pavement vibration response for evaluating roadway service condition. This paper presents a numerical model for the analysis of the pavement vibration due to the dynamic load created by a passing vehicle. A quarter vehicle model was used for the determination of the vehicle moving load. Both random and spatial characteristics of the load were considered. The random nonuniform moving load was then introduced in a 3D finite element model for the determination of the traffic-induced pavement vibration. The validated numerical model was used to assess the effects of dynamic load, material properties, and pavement structures on pavement vibration response. Numerical analyses showed that the vibration modes changed considerably for the different roadway service conditions. The vibration signals reflect the level of road roughness, the stiffness of the pavement materials, and the integrity of pavement structure. The acceleration extrema, the time-domain signal waveform, the frequency distribution, and the sum of squares of Fourier amplitude can be potential indexes for evaluating roadway service condition. This provides recommendations for the application of pavement vibration response in early-warning and timely maintenance of road.

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Monitoring Traffic Information with a Developed Acceleration Sensing Node

Cited by 24 publications

References 30 publications

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

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

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor

Investigating the Pavement Vibration Response for Roadway Service Condition Evaluation

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