On-Line Measuring System for Urban Highway Traffic Flow Based on Video Detection Technique

2010 ◽  
Vol 108-111 ◽  
pp. 713-717
Author(s):  
Chen Ning
Keyword(s):  
Traffic Flow ◽  
Recognition Rate ◽  
Transportation Systems ◽  
Detection Technique ◽  
Measuring System ◽  
Highway Traffic ◽  
Horizontal Projection ◽  
Moving Vehicle ◽  
Video Detection ◽  
On Line

In intelligent transportation systems (ITS) for urban highway-nets, the data of traffic flow is very important to control and lead to traffic. According to chromatic aberration between vehicles and road surfaces in images caught from moving vehicles, a new method is proposed to analyze on-line traffic states based on video detection technique. firstly video for 10 frame/second sampling, difference operation of same region of two neighbored image is used to detect moving vehicle very quickly on measurement strap; secondly the result of difference was grayed ,horizontal projection and adaptive threshold methods are adopted to recognize moving vehicle and type of vehicle by statistical analysis, which is very expedient, accurate and quick. The results of experiments show that the correct recognition rate of this system reaches up to 96%.And the combination of phase control of road control, which can intelligent lead to traffic by on-line and satisfies the requirements of practical application.

scholarly journals Road Traffic Analysis Using Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 2002-2006
Author(s):  
Needhi U. Gaonkar
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Visual Object ◽  
Highway Traffic ◽  
Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

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

scholarly journals An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

2020 ◽  
Vol 12 (20) ◽  
pp. 8298
Author(s):  
Zhanzhong Wang ◽  
Ruijuan Chu ◽  
Minghang Zhang ◽  
Xiaochao Wang ◽  
Siliang Luan
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Permutation Entropy ◽  
Support Vector ◽  
Gray Wolf ◽  
Highway Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

scholarly journals A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

2021 ◽  
Vol 11 (15) ◽  
pp. 6831
Author(s):  
Yue Chen ◽  
Jian Lu
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Counting Method ◽  
Moving Vehicle ◽  
Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

2018 ◽  
Vol 2672 (45) ◽  
pp. 196-209 ◽  
Author(s):  
Haozhe Cong ◽  
Cong Chen ◽  
Pei-Sung Lin ◽  
Guohui Zhang ◽  
John Milton ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Transportation Systems ◽  
Practical Significance ◽  
Incident Management ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

Rotating Accuracy Measurement System of Spindle Based on Three Point Method

2013 ◽  
Vol 333-335 ◽  
pp. 167-171
Author(s):  
Peng Qing Wang ◽  
Hong Jun Wang ◽  
He Peng Gao
Keyword(s):  
Personal Computer ◽  
Rotating Machinery ◽  
Point Method ◽  
Measuring System ◽  
Harmonic Suppression ◽  
Construction Site ◽  
Software Module ◽  
Accuracy Measurement ◽  
Sensor Arrangement ◽  
On Line

In order to measure the rotating machinery fault quickly and conveniently at a construction site, a new measuring system of rotating accuracy of spindle was developed according to three point method. With summarizing the principle of error separation technique based on three point method, the function, hardware construction and software module of this module were introduced. The problem of harmonic suppression is relieved by changing the sensor arrangement. The measuring system mainly consists of three eddy current sensors ,a acquisition card and a personal computer. After a few parameters were inputted, the rotation accuracy of calculation and display could be carried out automatically on line at a construction site.

scholarly journals EVALUATING THE EFFECT OF HEAVY VEHICLES ON TRAFFIC PARAMETERS BY USING HEAVY VEHICLE FACTOR

2018 ◽  
Vol 6 (4) ◽  
pp. 95-104
Author(s):  
Parthkumar Patel ◽  
H.R. Varia
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Freight Transport ◽  
Heavy Vehicle ◽  
Mixed Traffic ◽  
Heavy Vehicles ◽  
Highway Traffic ◽  
Ideal Situation ◽  
The Impact

Safe, convenient and timely transportation of goods and passengers is necessary for development of nation. After independence road traffic is increased manifold in India. Modal share of freight transport is shifted from Railway to roadways in India. Road infrastructures continuously increased from past few decades but there is still need for new roads to be build and more than three forth of the roads having mixed traffic plying on it. The impact of freight vehicles on highway traffic is enormous as they are moving with slow speeds. Nature of traffic flow is dependent on various traffic parameters such as speed, density, volume and travel time etc. As per ideal situation these traffic parameters should remain intact, but it is greatly affected by presence of heavy vehicle in mixed traffic due to Svehicles plying on two lane roads. Heavy vehicles affect the traffic flow because of their length and size and acceleration/deceleration characteristics.  This study is aimed to analyse the impact of heavy vehicles on traffic parameters.

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