A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

Zhang, Yingya; Ye, Ning; Wang, Ruchuan; Malekian, Reza

doi:10.3390/ijgi5050071

Cited by 35 publications

(21 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the threshold of traffic state discrimination was calibrated by simulation data. Zhang (2016) et al [7] took speed, traffic flow, and traffic density as data sets and defined the relational relationship between multidimensional attribute information based on grey relational analysis and rough set theory, established the grey relational clustering model, and then introduced the grey relational membership ranking algorithm (GMRC) to discriminate the clustering priority, so as to analyze the degree of road network congestion. Shang (2017) et al [8] constructed a traffic state discrimination model based on spectral clustering and stochastic subspace integration K-nearest neighbor (RS-KNN) in order to improve the accuracy of urban expressway traffic state discrimination.…”

Section: The Methods Based On Data Miningmentioning

confidence: 99%

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

Lin

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: The Methods Based On Data Miningmentioning

confidence: 99%

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

Lin

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Three papers on ITS are as follows: (1) "Vehicle positioning and speed estimation based on cellular network signals for urban roads," by Lai and Kuo [41]; (2) "A method for traffic congestion clustering judgment based on grey relational analysis," by Zhang et al [42]; and (3) "Smartphone-based pedestrian's avoidance behavior recognition towards opportunistic road anomaly detection," by Ishikawa and Fujinami [43].…”

Section: Intelligent Transportation Systemsmentioning

confidence: 99%

“…The proposed method based on grey relational analysis can obtain the membership degree rank of classes for judging the rank of data objects and improving the accuracy of traffic congestion detection. In experimental environments, the practical traffic flow records were collected from 30 drivers to evaluate the proposed method, and the results showed that the average accuracy of the proposed algorithm was 24.9% higher than that of the K-means algorithm [42].…”

Section: Intelligent Transportation Systemsmentioning

confidence: 99%

“…Therefore, the aim of this special issue is to introduce the readers a number of papers on various aspects of IoT applications. This special issue has received a total of 23 submitted papers with only 8 papers [41][42][43][44][45][46][47][48] accepted. A high rejection rate of 65.21% of this issue from the review process is to ensure that high-quality papers with significant results are selected and published.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Internet of Things

Chen

2018

IJGI

View full text Add to dashboard Cite

This editorial introduces the special issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITS), (II) location-based services (LBS), and (III) sensing techniques and applications. Three papers on ITS are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBS are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.

show abstract

“…Moreover, grey relational analysis, which is another active branch of grey system theory, has been successfully applied to management science and industrial control in practice [33,34]. Zhang et al [35] used grey relational analysis for traffic congestion clustering judgment and obtained good results.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

View full text Add to dashboard Cite

This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

show abstract

A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

Cited by 35 publications

References 10 publications

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

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

Applications of Internet of Things

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Contact Info

Product

Resources

About