Spatial analysis of traffic accidents based on WaveCluster and vehicle communication system data

The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and...

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Vydáno v:EURASIP journal on wireless communications and networking Ročník 2019; číslo 1; s. 1 - 10
Hlavní autoři: Zhang, Junhui, Shi, Tuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 22.05.2019
Springer Nature B.V
SpringerOpen
Témata:
Accident data
Cluster analysis
Clustering
Communications Engineering
Communications systems
Differentiation
Distributed Big Data Processing in SCADA Communication Networks
Drivers
Engineering
Information systems
Information Systems Applications (incl.Internet)
K-means clustering
Laws
Networks
Optimization
Rural roads
Safety management
Secure vehicle communication system
Signal,Image and Speech Processing
Spatial analysis
Spatial differentiation
Traffic accidents
Traffic accidents & safety
Traffic management
Traffic safety
Traffic volume
Vector quantization
Wavelet analysis
Wavelet analysis
means clustering
Spatial differentiation
Secure vehicle communication system
ISSN:1687-1499, 1687-1472, 1687-1499
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Abstract The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k -means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.
AbstractList The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k-means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.
Abstract The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k-means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.
The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k -means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.
ArticleNumber 124
Author Shi, Tuo
Zhang, Junhui
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  givenname: Tuo
  surname: Shi
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  organization: Beijing Police College
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10.1049/iet-net.2013.0065
10.1016/j.aap.2008.12.014
10.1016/j.trf.2015.04.008
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Keywords Wavelet analysis
means clustering
Spatial differentiation
Secure vehicle communication system
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Abstract The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and...
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SubjectTerms Accident data
Cluster analysis
Clustering
Communications Engineering
Communications systems
Differentiation
Distributed Big Data Processing in SCADA Communication Networks
Drivers
Engineering
Information systems
Information Systems Applications (incl.Internet)
K-means clustering
Laws
Networks
Optimization
Rural roads
Safety management
Secure vehicle communication system
Signal,Image and Speech Processing
Spatial analysis
Spatial differentiation
Traffic accidents
Traffic accidents & safety
Traffic management
Traffic safety
Traffic volume
Vector quantization
Wavelet analysis
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Title Spatial analysis of traffic accidents based on WaveCluster and vehicle communication system data
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