基于C5.0决策树的船舶交通事故致因分析模型及应用

doi:10.16265/j.cnki.issn1003-3033.2022.10.0483

摘要/Abstract

摘要：

为减少船舶交通事故的发生,对船舶交通事故的致因展开研究。首先,以事故类型作为输出变量,以船舶交通事故数据为样本,构建基于C5.0算法的船舶交通事故致因路径分析模型;然后,确定事故致因路径分析有效性评价指标;再次,运用“2-4”模型(24Model),对所识别出的不同类型事故致因路径因果关系进一步分析,提出通过切断事故潜在致因路径的船舶交通事故预控措施;最后,将894起船舶交通事故数据样本随机分为80%的训练集和20%的测试集,应用所提出的模型进行分析。结果表明:所提出的模型可以生成不同类型事故的分类规则集,模型分类正确率达到90%以上,且模型具有强的泛化能力。结合分类规则集构建的船舶交通事故致因链为船舶交通事故的防范提供定量化的理论依据。

关键词: C5.0算法, 决策树, 船舶交通事故, 致因路径, 致因分析, “2-4”模型(24Model)

Abstract:

In order to reduce the occurrence of ship accidents, the causation of ship traffic accidents was analyzed. Firstly, a ship traffic accident causation path analysis model based on the C5.0 algorithm was constructed. The model took accident type as the output variable and the ship traffic accident data as samples. Then, the evaluation indexes of validity for path analysis of accident causation were established. Furthermore, the 24Model was used to analyze the causal relationship of different types of accident paths. Finally, the prevention and control measures for ship traffic accidents were proposed by cutting off the potential causation path of accidents. Taking 894 ship traffic accidents as an example, the sample set was randomly divided into 80% training set and 20% test set, and the proposed model was used for analysis. The research results show that the proposed model can generate a set of classification rules for different types of accidents, the classification accuracy rate of the model is over 90%, and the model has a strong generalization ability. The causal chain of ship traffic accidents constructed in combination with the classification rule set provides a quantitative theoretical basis for the prevention of accidents.

Key words: C5.0 algorithms, decision tree, ship traffic accident, causation path, causation analysis, 24Model

黄常海, 沈佳, 朱冉超, 齐绪存, 郑菲, 陆浩. 基于C5.0决策树的船舶交通事故致因分析模型及应用[J]. 中国安全科学学报, 2022, 32(10): 90-99.

HUANG Changhai, SHEN Jia, ZHU Ranchao, QI Xucun, ZHENG Fei, LU Hao. Causation analysis model for ship traffic accidents based on C5.0 decision tree and application[J]. China Safety Science Journal, 2022, 32(10): 90-99.

图/表 8

图1

图2

表1

图3

表2

分类规则集

序号	输出变量	分类规则集	样本量	置信度/%
1	碰撞	大风浪、港区泊位附近水域、船长不在驾驶台	7	86
2		大风浪、违规操作、航行、过往船或进出口船	9	100
3		大风浪、避让与操纵行为过失、航行或锚泊	18	94
4		大风浪、避让与操纵行为过失、船长在驾驶台、岛礁区或海上或闽中渔场	10	80
5		非大风浪、水上水下作业	17	77
6		非大风浪、航行、应急处理过失或通信合作过失或避让与操纵行为过失	170	94
7		非大风浪、航行、公务船	32	84
8		非大风浪、航行、货船、XM辖区或ND辖区或ZZ辖区或FZ辖区	27	70
9		非大风浪、航行、货船、QZ辖区、雾季	11	73
10		非大风浪、航行、自然条件	11	91
11		非大风浪、航行、航行操作过失、XM辖区、港区泊位附近水域	8	75
12		非大风浪、航行、航行操作过失、ZZ辖区或FZ辖区	7	86
13		非大风浪、航行、违规操作、公务船或客船	21	86
14		非大风浪、航行、违规操作、货船、海上、能见度不良或受限制	11	91
15		非大风浪、航行、违规操作、货船、港区泊位附近水域或锚地或闽中渔场	29	86
16		非大风浪、锚泊、港内作业船、港区泊位附近水域或锚地	15	87
17		非大风浪、锚泊、进出口船	26	89
18		通航环境、能见度不良、港区泊位附近水域或锚地	7	100
19		潜在缺陷、非大风浪、能见度不良或良好	6	83
20		设备缺陷、避让与操纵行为过失、非大风浪、海上或锚地	6	100
序号	输出变量	分类规则集	样本量	置信度/%
21	触礁	助航设施	5	80
22		可航水域、台风或寒潮	5	100
23		大风浪、航线计划过失、雾季	10	80
24		通航环境、能见度良好、自然条件	23	83
25		设备缺陷、避让与操纵行为过失、非大风浪、港区泊位附近水域	5	80
26		设备缺陷、航行操作过失	8	88
27	风灾	大风浪、应急处理过失、能见度不良	10	70
28		大风浪、海上、ND辖区或PT辖区	10	80
29		货物缺陷、自然条件、公司管理局限或船内外作业方	5	80
30	火灾爆炸	非大风浪、锚泊、公司管理局限、台风或雾季	5	80
31	自沉	设备缺陷、航行、海上、寒潮	5	80
32	自沉	超载、台风或雾季	6	100

表2

图4

表3

图5

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序号	变量类别	输入变量	事故变量具体说明
1	事故属性	季节特征	寒潮、台风、雾季
2		地点类型	岛礁区、港区泊位附近水域、海上、锚地、闽中渔区
3		管辖水域	FZ辖区、ND辖区、PT辖区、QZ辖区、XM辖区、ZZ辖区
4		船舶种类	公务船、客船、货船、不明
5		航线	进出口船、过往船、港内作业船
6		船舶动态	并靠、航行、锚泊、水上水下作业
序号	变量类别	输入变量	事故变量具体说明
7	事故致因	引航员操作	是、否
8		船长在驾驶台	船长在驾驶台、船长不在驾驶台
9		其他人员	船内外作业方、拖轮作业方、公司管理局限、海事管理局限、无关
10		船员原因	违规操作、航行操作过失、航线计划过失、通信合作过失、应急处理过失、避让与操纵行为过失、无关
11		能见度	能见度不良、能见度良好、能见度受限制
12		风浪	大风浪、非大风浪
13		意外原因	自然条件、船舶条件、其他条件、无关
14		交通原因	泊位锚地、交通意外、通航秩序、无关
15		航道码头原因	渔区、助航设施、航道弯道、可航水域、通航环境、海图出版物、无关
16		船舶货物原因	超载、货物缺陷、潜在缺陷、结构缺陷、设备缺陷、无关

分区	训练集	测试集
样本量	715	179
分类正确率/%	91.05	93.85
置信度范围	0.402~1.000	0.352~1.000
平均正确性	0.813	0.825