Road traffic accident data mining based on grey relational clustering

被引：0
|
作者：
Liu, Y. [1 ]
Xu, H. [2 ]
Zhang, C. [1 ]
Shi, X.D. [3 ]
Patnaik, S. [4 ]
机构：
[1] Cloud Computing and Big Data Institute, Henan University of Economics and Law, Henan, Zhengzhou,450046, China
[2] Henan Key Laboratory of Ecological Environment Protection and Restoration of the Yellow River Basin, Yellow River Institute of Hydraulic Research, Henan, Zhengzhou,450003, China
[3] School of 'E-commerce and Logistics Management, Henan University of Economics and Law, Zhengzhou,450046, China
[4] Department of Computer Science and Engineering, SOA University, Bhubaneswar,751030, China
来源：
Advances in Transportation Studies | 2023年 / 3卷 / Special issue期
关键词：
Cluster analysis - Factor analysis - Fault tree analysis - Highway accidents - Roads and streets - Trees (mathematics);
D O I：
10.53136/979122180922010
中图分类号：
学科分类号：
摘要：
Data mining can effectively identify and discover the patterns and inherent laws of accident data. The paper proposes a road traffic accident data mining method based on grey relational clustering. Determine the key influencing factors of road traffic accidents through fault tree analysis method, and achieve accurate quantification of road traffic accident data. Extract the features of road traffic accident data based on EM algorithm. Set the grey relationship analysis factor for road traffic accident data, create a sequence of behavioral feature data, and determine the grey relationship sequence by the operator. Using whitening weight functions to cluster the features of accident data, classify data with consistent features, and achieve road traffic accident data mining. The experimental results show that the designed method has good sensitivity and high grey correlation coefficient in road traffic accident data mining, indicating the feasibility of this method. © 2023, Aracne Editrice. All rights reserved.
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页码：113 / 124
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