Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network
The analysis of severity causality for traffic crash is essential for enhancing the crash rescue responding speed, thereby reducing the casualties and property losses caused by roadway crashes. This study constructs a severity causation network to explore the relationship between risk factors and cr...
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doaj-fd2d628ef9424c81b0eee51f1a75bc6d2021-03-29T22:57:51ZengIEEEIEEE Access2169-35362019-01-017632886330210.1109/ACCESS.2019.29166918713967Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian NetworkFang Zong0Xiangru Chen1https://orcid.org/0000-0002-1449-7113Jinjun Tang2Ping Yu3Ting Wu4College of Transportation, Jilin University, Changchun, ChinaCollege of Transportation, Jilin University, Changchun, ChinaSchool of Traffic and Transportation Engineering, Central South University, Changsha, ChinaCollege of Transportation, Jilin University, Changchun, ChinaCollege of Transportation, Jilin University, Changchun, ChinaThe analysis of severity causality for traffic crash is essential for enhancing the crash rescue responding speed, thereby reducing the casualties and property losses caused by roadway crashes. This study constructs a severity causation network to explore the relationship between risk factors and crash severity by combining information entropy and Bayesian network. The impacts of different risk factors on the severity indexes are quantitatively estimated and compared by utilizing entropy weight method, and the key factors for severity prediction are determined by considering both the weight and the accessibility. Then, the severity indexes, i.e., the number of injuries, fatalities, and the amount of property damage, are predicted with the selected key factors based on Bayesian parameter learning. The verification results confirm that compared with severity prediction utilizing all the risk factors, the prediction utilizing selected key factors do not lead to obviously precision loss. Moreover, it significantly enhances the feasibility of crash severity prediction. Due to the appropriate abbreviation of risk factors, the prediction efficiency and practical operability in crash rescue responding is improved. The findings can be utilized in analyzing the severity causation of traffic crashes, which is serviceable for managers to find effective measures to improve traffic safety as well as reduce the casualties and property losses caused by traffic crashes. By providing the severity causation network, this study facilitates the prediction of severity level, which can provide valuable reference information for a crash response.https://ieeexplore.ieee.org/document/8713967/Crash severity predictionseverity causalityrisk analysisinformation entropyBayesian methods |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fang Zong Xiangru Chen Jinjun Tang Ping Yu Ting Wu |
spellingShingle |
Fang Zong Xiangru Chen Jinjun Tang Ping Yu Ting Wu Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network IEEE Access Crash severity prediction severity causality risk analysis information entropy Bayesian methods |
author_facet |
Fang Zong Xiangru Chen Jinjun Tang Ping Yu Ting Wu |
author_sort |
Fang Zong |
title |
Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network |
title_short |
Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network |
title_full |
Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network |
title_fullStr |
Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network |
title_full_unstemmed |
Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network |
title_sort |
analyzing traffic crash severity with combination of information entropy and bayesian network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
The analysis of severity causality for traffic crash is essential for enhancing the crash rescue responding speed, thereby reducing the casualties and property losses caused by roadway crashes. This study constructs a severity causation network to explore the relationship between risk factors and crash severity by combining information entropy and Bayesian network. The impacts of different risk factors on the severity indexes are quantitatively estimated and compared by utilizing entropy weight method, and the key factors for severity prediction are determined by considering both the weight and the accessibility. Then, the severity indexes, i.e., the number of injuries, fatalities, and the amount of property damage, are predicted with the selected key factors based on Bayesian parameter learning. The verification results confirm that compared with severity prediction utilizing all the risk factors, the prediction utilizing selected key factors do not lead to obviously precision loss. Moreover, it significantly enhances the feasibility of crash severity prediction. Due to the appropriate abbreviation of risk factors, the prediction efficiency and practical operability in crash rescue responding is improved. The findings can be utilized in analyzing the severity causation of traffic crashes, which is serviceable for managers to find effective measures to improve traffic safety as well as reduce the casualties and property losses caused by traffic crashes. By providing the severity causation network, this study facilitates the prediction of severity level, which can provide valuable reference information for a crash response. |
topic |
Crash severity prediction severity causality risk analysis information entropy Bayesian methods |
url |
https://ieeexplore.ieee.org/document/8713967/ |
work_keys_str_mv |
AT fangzong analyzingtrafficcrashseveritywithcombinationofinformationentropyandbayesiannetwork AT xiangruchen analyzingtrafficcrashseveritywithcombinationofinformationentropyandbayesiannetwork AT jinjuntang analyzingtrafficcrashseveritywithcombinationofinformationentropyandbayesiannetwork AT pingyu analyzingtrafficcrashseveritywithcombinationofinformationentropyandbayesiannetwork AT tingwu analyzingtrafficcrashseveritywithcombinationofinformationentropyandbayesiannetwork |
_version_ |
1724190503881146368 |