scholarly journals Quantitative Analysis of Urban Regional Traffic Status

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qing-fang Yang ◽  
Ru-ru Xing ◽  
Li-li Zheng ◽  
Shu-xing Wang
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Road Network ◽  
Urban Region ◽  
Operation Condition ◽  
Traffic Condition ◽  
Traffic State ◽  
Time Operation ◽  
State Classification ◽  
Real Time Operation

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

A Fuzzy Logic Method of Traffic State Recognition on Urban Traffic Networks

2012 ◽  
Vol 546-547 ◽  
pp. 1071-1074
Author(s):  
Jian Ling Wang ◽  
Hong Bo Lai
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Traffic Networks ◽  
Traffic Condition ◽  
Time Length ◽  
Traffic State ◽  
Fuzzy Logic Method ◽  
Logic Method ◽  
Urban Traffic Networks

The study object is traffic flow on main road of urban traffic networks, the traffic condition is recognized by traffic flow theory and fuzzy logic method. The average space speed is a variable of the fact flow function, the road congestion degree is described by the ratio of fact flow and traffic capacity; the ratio of congestion time length and total time length is the congestion frequency. Considering congestion degree and congestion frequency, a fuzzy logic method is used to describe the traffic state by three grades: free, congestion and serious congestion. At last, the numerical example is given to analyze traffic state.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

scholarly journals Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Senyan Yang ◽  
Lianju Ning ◽  
Xilong Cai ◽  
Mingyu Liu
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Network Traffic ◽  
Road Network ◽  
Time Series Data ◽  
Sliding Window ◽  
Transfer Entropy ◽  
Series Data ◽  
Window Approach ◽  
Road Segments

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

scholarly journals A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaohui Lin
Keyword(s):  
Data Mining ◽  
Network Traffic ◽  
Spectral Clustering ◽  
Road Network ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Mining Method ◽  
Traffic State ◽  
State Identification ◽  
Spectral Clustering Algorithm

Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.

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