|本期目录/Table of Contents|

[1]何庆龄,裴玉龙,刘静,等.基于WOA-LSSVM的城市道路交通事故严重度识别*[J].中国安全生产科学技术,2023,19(9):176-182.[doi:10.11731/j.issn.1673-193x.2023.09.026]
　HE Qingling,PEI Yulong,LIU Jing,et al.Recognition of urban road traffic accident severity based on WOA-LSSVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(9):176-182.[doi:10.11731/j.issn.1673-193x.2023.09.026]
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基于WOA-LSSVM的城市道路交通事故严重度识别*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
19
期数:
2023年9期
页码:
176-182
栏目:
职业安全卫生管理与技术
出版日期:
2023-09-30

文章信息/Info

Title:
Recognition of urban road traffic accident severity based on WOA-LSSVM
文章编号:
1673-193X(2023)-09-0176-07
作者:
何庆龄; 裴玉龙; 刘静; 张杰; 潘胜
（东北林业大学 土木与交通学院，黑龙江 哈尔滨 150040）
Author(s):
HE Qingling;  PEI Yulong;  LIU Jing;  ZHANG Jie;  PAN Sheng
(College of Civil Engineering and Transportation,Northeast Forestry University,Harbin Heilongjiang 150040,China)
关键词:
城市交通; 事故严重度; 鲸鱼优化算法; 最小二乘支持向量机
Keywords:
urban road traffic;  accident severity;  whale optimization algorithm (WOA);  least squares support vector machine (LSSVM)
分类号:
X951
DOI:
10.11731/j.issn.1673-193x.2023.09.026
文献标志码:
A
摘要:
为预防交通安全事故，提高城市道路交通事故严重度识别正确率和适用性，基于221起城市道路交通事故数据，选择16个城市道路交通事故严重度影响因素作为特征变量，通过对连续特征变量统计分析和离散特征变量进行赋值，构建基于WOA-LSSVM的城市道路交通事故严重度识别模型。研究结果表明：一般事故肇事者年龄、驾龄和车辆车速均值最大，分别为45岁、99个月和51.6 km/h；重大事故车辆服役时间均值最大，为57.5个月；当WOA-LSSVM模型的迭代次数为30、种群规模为300时，对应城市道路交通事故严重度识别正确率、精确率、召回率和F1值分别为95.6%、95.3%、94.9%和94.7%，相较于LSSVM模型分别增加15.6%、16.4%、14.6%和18.3%，有效提高轻微事故识别的有效性。研究结果可为制定城市道路交通事故安全风险防控措施提供理论依据。
Abstract:
To prevent the traffic safety accidents and improve the correctness and applicability of severity recognition of the urban road traffic accidents,based on the data of 221 urban road traffic accidents,16 influencing factors of urban road traffic accident severity were selected as the feature variables.After the statistical analysis of continuous feature variables and assignment of discrete feature variables,a recognition model of urban road traffic accident severity based on WOA-LSSVM was constructed.The results show that the general accident perpetrators had the highest mean values of age,driving age and vehicle speed,which are 45 years,99 months and 51.6 km/h respectively.The average service time of vehicle in major accidents is the largest,which is 57.5 months.The WOA-LSSVM model has the best overall performance when the number of iterations and population size are set to 30 and 300,and the corresponding recognition correctness,accuracy,recall and F1 values of urban road traffic accident severity are 95.6%,95.3%,94.9% and 94.7% respectively,which increase by 15.6%,16.4%,14.6% and 18.3% respectively compared to the LSSVM model,so it can effectively improve the effectiveness of minor accident recognition.The research results can provide a theoretical basis for the development of safety risk prevention and control measures on the urban road traffic accidents.

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备注/Memo

备注/Memo:
收稿日期： 2023-04-25
* 基金项目： 中央高校基本科研业务费专项资金项目（2572022AW62）；国家重点研发计划项目（2018YFB1600902）
作者简介： 何庆龄，博士研究生，主要研究方向为交通安全、智能算法。
通信作者： 裴玉龙，博士，教授，主要研究方向为交通安全。
更新日期/Last Update: 2023-10-12