Driving Mode at Pothole-Subsidence Pavement Based on Wheel Path

2012 ◽  
Vol 524-527 ◽  
pp. 847-851 ◽  
Author(s):  
Yu Long Pei ◽  
Cheng Yuan Mao ◽  
Mo Song
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Asphalt Pavement ◽  
Average Rate ◽  
Driving Mode ◽  
Road Section ◽  
Lateral Distance ◽  
Video Detection ◽  
Normal Diameter ◽  
Vehicle Trajectory

Considering the fact that the forms of asphalt pavement potholes, subsidence and cement pavement potholes (collectively defined as pavement pothole-subsidence) are similar and they can influence traffic flow significantly, we put forward to use indexes such as Tangential Diameter Length, Normal Diameter Length, Depth, Lateral distance, etc to describe the characteristics of pothole-subsidence, and we also adopt AutoScope-2004 video detection system aided by artificial judging to investigate in the surveyed road section. According to different wheel paths, driving modes was classified into three types, influences of various pothole-subsidence on driving mode and speed was analyzed. We came up with conclusions as follows: one is that pothole-subsidence significantly influenced the variation of vehicle trajectory, 78.5% vehicles altered their driving direction, and the average rate of speed descent is over 20%.

scholarly journals Driving Mode at Pothole-Subsidence Pavement Based on Wheel Path & Speed

2012 ◽  
Vol 5 ◽  
pp. 77-81
Author(s):  
Yu Long Pei ◽  
Cheng Yuan Mao ◽  
Mo Song
Keyword(s):  
Traffic Flow ◽  
Asphalt Pavement ◽  
Considerable Influence ◽  
Pavement Distress ◽  
Driving Mode ◽  
Lateral Distance ◽  
Normal Diameter

The pavement distress has considerable influence with drivers. Considering the fact that the forms of asphalt pavement potholes, subsidence and cement pavement potholes (collectively defined as pavement pothole-subsidence) are similar and they can influence traffic flow significantly, we put forward to use indexes such as Tangential Diameter Length, Normal Diameter Length, Depth, Lateral distance, etc to describe the characteristics of pothole-subsidence. According to different wheel paths & speed, driving modes was classified into several types, influences of various pothole-subsidence on driving mode and speed was analyzed.

A Novel Video Detection System on Traffic Flow Inspection

2012 ◽  
Vol 182-183 ◽  
pp. 440-444
Author(s):  
Zhan Wen Liu ◽  
Shan Lin ◽  
Sheng Gen Dou
Keyword(s):  
Image Segmentation ◽  
Traffic Flow ◽  
Clustering Algorithm ◽  
Dynamic Range ◽  
Detection System ◽  
Image Data ◽  
Image Sensor ◽  
Imaging Device ◽  
The Core ◽  
Video Detection

A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomplished easily to segment the image. The simulation results show that the row scan speed of this system can reach to 1000 lines per second, the resolution being 2048 pixels.

Traffic Flow Video Detection System Based on Line Scan CMOS Sensor

2012 ◽  
Vol 7 (1) ◽  
pp. 478-483 ◽  
Author(s):  
Zhanwen Liu ◽  
Shan Lin ◽  
Kunlun Li ◽  
Anguo Dong
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Line Scan ◽  
Cmos Sensor ◽  
Video Detection ◽  
On Line

Automatic Extraction of Multi-Vehicle Trajectory Based on Traffic Videotaping from Quadcopter Model

2014 ◽  
Vol 552 ◽  
pp. 232-239 ◽  
Author(s):  
Hang Gao ◽  
Si Li Kong ◽  
Siao Zhou ◽  
Fang Lv ◽  
Quan Chen
Keyword(s):  
Traffic Flow ◽  
Traffic Engineering ◽  
Detection System ◽  
Driving Behavior ◽  
Automatic Extraction ◽  
New Approach ◽  
Behavior Monitoring ◽  
Vehicle Trajectory ◽  
Practical Field

This dissertation proposes a new approach for vehicular trajectory detecting. A radio-controlled quadcopter is used to shoot live traffic flow videos which can be flexible enough to meet the requirements of various road conditions. A self-developed software is created to analyze traffic videos efficiently, which can extract the coordinate of each vehicle from the video and draw the trajectories of those vehicles automatically. The system only produces a relatively small bias, which is allowed in the practical field of traffic engineering. The proposed detection system can not only get the trajectory of vehicle conveniently, but also provide an easy way to collect the data on the velocity and the acceleration of vehicles and, even, set a foundation for driving behavior monitoring and analysis on the roads.

Multi-lane traffic flow monitoring and detection system based on video detection

2020 ◽  
Vol 38 (2) ◽  
pp. 1287-1298
Author(s):  
Xue Liu ◽  
Xiaowei Wang ◽  
Zhaosheng Yang
Keyword(s):  
Traffic Flow ◽  
Detection System ◽  
Flow Monitoring ◽  
Video Detection

scholarly journals Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Shing Tenqchen ◽  
Yen-Jung Su ◽  
Keng-Pin Chen
Keyword(s):  
Traffic Flow ◽  
Model Verification ◽  
Traffic Information ◽  
Fiscal Year ◽  
Detection Model ◽  
Road Section ◽  
Trial Field ◽  
Flow Information ◽  
Model Verification And Validation ◽  
Traffic Collection

This paper proposes a using Cellular-Based Vehicle Probe (CVP) at road-section (RS) method to detect and setup a model for traffic flow information (info) collection and monitor. There are multiple traffic collection devices including CVP, ETC-Based Vehicle Probe (EVP), Vehicle Detector (VD), and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem, monitor and control. The main project has been applied at Tai # 2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018. This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory (LTSM) from recurrent neural network (RNN) model. We also provide a model verification and validation methodology with RNN for cross verification of system performance.

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