基于AIS通信量的水上交通事故检测方法

吴建华; 彭虎; 王辰; 付鹏

doi:10.3963/j.jssn.1674-4861.2023.05.009

基于AIS通信量的水上交通事故检测方法

doi: 10.3963/j.jssn.1674-4861.2023.05.009

吴建华^{1, 2,},
彭虎¹,
王辰^3, ,,
付鹏⁴

1.
武汉理工大学航运学院武汉 430063
2.
武汉理工大学内河航运技术湖北省重点实验室武汉 430063
3.
交通运输部规划研究院北京 100020
4.
中交水运规划设计有限公司北京 100007

基金项目:

国家自然科学基金项目 52271366

详细信息

作者简介:
吴建华(1963—)博士，教授.研究方向：交通信息工程及控制.E-mail wujh63@sina.com

通讯作者:
王辰(1978—)，硕士，高级工程师. 研究方向：交通安全.E-mail: 2634543@qq.com

中图分类号: U698.6
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-11
- 网络出版日期: 2024-01-18

A Detection Method for Maritime Traffic Accidents Based on AIS Communication Volume

WU Jianhua^{1, 2
,},
PENG Hu¹,
WANG Chen^{3
, ,},
FU Peng⁴

1.
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
2.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
3.
Transport Planning and Research Institute, Ministry of Transport, Beijing 10020, China
4.
China Communications Planning & Design Institute for Water Transportation, Beijing 100007, China

摘要

摘要: 基于数据驱动的交通事故检测对水上交通事故的快速救援与降低事故损失具有重要作用。为实现无自主报告情形下的交通事故自动检测，研究了基于船舶自动识别系统(automatic identification system，AIS) 通信量的水上交通事故检测方法。通常情况下，突发的水上交通事故会扰乱船舶正常航行秩序，事故引起的船舶运动状态改变会导致AIS通信量短时间内产生突变，为挖掘AIS通信量随水上交通事故演变的内在规律，并降低噪声影响，以凸显检测指标的突变特征，引入AIS通信量和船舶总数比值构建水上交通事故检测指标；为确保检测的时效性，采用滑动窗口模型用于划分检测指标数据片段与设定更新时间的间隔，并构建基于卡尔曼滤波的水上交通事故检测模型进行检测指标的短期预测；为保证检测结果的准确性，采用云模型进行检测模型阈值范围的快速划分。使用长江武汉段水域内AIS数据对基于AIS通信量的水上交通事故检测方法进行模型验证和仿真研究，实验结果表明：与采用标准正态偏差和多尺度直线拟合算法的检测模型相比，提出的基于卡尔曼滤波算法的检测模型可在耗时最短的情形下获得最高命中率与最低误检率，分别为97.25%与0.42%；在进一步的仿真实验中，针对3种不同的船舶事故场景，提出的基于AIS通信量的水上交通事故检测方法均能在5 min内检测出水上交通事故的发生。
- 交通安全 /
- 水上交通事故 /
- 水上交通事故检测方法 /
- 卡尔曼滤波 /
- AIS通信量
Abstract: The data-driven approach for traffic accident detection plays a crucial role in the rapid rescue and reduction of losses in maritime accidents. To achieve automatic detection of maritime accidents without autonomous reporting, a method based on Automatic Identification System (AIS) communication volume is proposed. Normally, sudden maritime accidents disrupt the normal navigation patterns of vessels, leading to sharp changes in AIS communication volume within a short time due to changes in vessel movement states during the accidents. To extract the inherent laws of AIS communication volume during the evolution of maritime accidents and reduce noise interference to highlight the abrupt features of detection indicators, the AIS communication volume-to-total vessel count ratio is introduced as an indicator for maritime accident detection. To ensure timely detection, a sliding window model is used to segment detection indicator data and set update time intervals. Furthermore, a maritime accident detection model based on Kalman filtering is developed for short-term prediction of detection indicators. To ensure the accuracy of detection results, a cloud model is employed for rapid division of detection model threshold ranges. Validations and simulations are conducted using AIS data from the Yangtze River Wuhan section to verify the AIS communication volume-based maritime accident detection method. Results show that the proposed detection model based on Kalman filtering achieves the highest hit rate and lowest false alarm rate in the shortest time, namely 97.25% and 0.42%, respectively, compared to models employing standard normal deviation and multi-scale linear fitting algorithms. In further simulated experiments involving three different accident scenarios, the proposed AIS communication volume-based maritime accident detection method successfully detects accidents within 5 minutes.
- traffic safety /
- maritime traffic accident /
- maritime traffic accident detection method /
- Kalman filter /
- AIS communication volume

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图 1 船舶群组内船舶数占比变化对AIS通信量影响

Figure 1. The influence of changes in the proportion of ships in a ship group on AIS communication volume

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图 2 AIS通信量及船舶总数变化趋势图

Figure 2. Trend chart of AIS communication volume and total number of ships

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图 3 AIS通信量-船舶总数散点分布图

Figure 3. AIS communication volume - total number of ships scatter distribution map

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图 4 AIS通信量和水上交通事故检测指标的变化曲线

Figure 4. Variation curves of AIS communication volume and maritime traffic accident detection indicators

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图 5 AIS通信量Ne(t) 分布统计

Figure 5. Distribution statistics of AIS communication volume Ne(t)

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图 6 水上交通事故检测指标Ne_M(t) 分布统计

Figure 6. Distribution statistics of Ne_M(t) detection index for maritime traffic accidents

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图 7 基于云模型的检测阈值确定流程

Figure 7. Detection threshold determination process based on cloud model

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图 8 基于AIS通信量的水上交通事故检测方法流程

Figure 8. Maritime traffic accident detection method process based on AIS communication volume

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图 9 具有突变特征的模拟时序数据

Figure 9. Simulating time series data with abrupt changes

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图 10 3种算法检测结果示意图

Figure 10. Schematic diagram of the detection results of the three algorithms

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图 11 水上交通事故检测仿真流程图

Figure 11. Flow chart of maritime traffic accident detection simulation

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图 12 船舶运动模拟软件操作流程

Figure 12. Operating procedure for ship motion simulation software

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图 13 船载A类和B类AIS设备的数量关系

Figure 13. The relationship between the number of class A and class B AIS devices on ships

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图 14 A类船舶数量关系

Figure 14. The number of class A ships relationship

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图 15 B类船舶数量关系

Figure 15. The number of class B ships relationship

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图 16 航行速度概率密度分布

Figure 16. Probability density distribution of navigational speed

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图 17 场景1输出的水上交通事故检测指标及预测残差

Figure 17. The detection indicators and prediction residuals of maritime traffic accidents output in Scenario 1

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图 18 场景2输出的水上交通事故检测指标及预测残差

Figure 18. The detection indicators and prediction residuals of maritime traffic accidents output in Scenario 2

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图 19 场景3输出的水上交通事故检测指标及预测残差

Figure 19. The detection indicators and prediction residuals of maritime traffic accidents output in Scenario 3

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表 1 AIS动态信息更新间隔表

Table 1. AIS dynamic information update interval table

AIS终端类型	船舶状态	更新间隔
A类AIS	抛锚、停泊且速度≤ 3 n mile/h	3 min
	抛锚、停泊且速度 > 3 n mile/h	10 s
	航行速度0~14 n mile/h	10 s
	航行速度0~14 n mile/h并改变航向	3.3 s
	航行速度14~23 n mile/h	6 s
	航行速度14~23 n mile/h并改	2 s
	变航向	2 s
	航行速度 > 23 n mile/h	2 s
	航行速度 > 23 n mile/h并改变航向	3 min
B类AIS(SOTDMA)	航行速度≤ 2 n mile/h	3 min
	辅助导航	30 s
	航行速度2~14 n mile/h	15 s
	航行速度14~23 n mile/h	5 s
	航行速度 > 23 n mile/h	3 min
B类AIS(CSTDMA)	航行速度≤ 2 n mile/h	30 s
B类AIS(CSTDMA)	航行速度 > 2 n mile/h

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表 2 突变数据流片段设定

Table 2. Mutation data flow fragment setting

突变数据流片段	开始时间	结束时间
片段1	09∶20	10∶10
片段2	12∶40	13∶30
片段3	16∶00	16∶50

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表 3 3种算法的阈值范围

Table 3. Threshold ranges for the three algorithms

参数名称	SND检测算法	多尺度直线拟合算法	Kalman滤波算法
期望	-1.7×10^-²	-2.6×10^-⁵	-4.2×10^-⁴
熵	1.274 4	0.016 5	0.146 3
超熵	0.060 1	4.7×10^-4	0.005 3
阈值上限	3.806 2	0.047 8	0.438 6
阈值下限	-3.840 3	-0.047 9	-0.439 4

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表 4 3种检测算法模拟测试的评价指标统计表

Table 4. Statistical table of evaluation indicators for simulation tests of three detection algorithms

算法	命中率/%	误检率/%	时耗/s
SND算法	92.00	1.25	0.027
多尺度直线拟合算法	66.67	1.67	2.199
卡尔曼滤波算法	97.25	0.42	0.005

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表 5 事故时间及位置设置

Table 5. Accident time and location setting

场景设置	事故发生时刻	事故消失时刻	事故位置
场景1	08：00	08：30	114.3384°E，30.6233°N
场景2	11：27	11：55	114.3671°E，30.6436°N
场景3	16：22	16：55	114.4177°E，30.6632°N

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表 6 船舶分配及航速设置

Table 6. Ship allocation and speed setting

船舶类型	船舶总数/艘	具有救援能力船舶/艘	初始航速/(n mile/h)
A类AIS设备船舶	9	2	7
B类AIS设备船舶	21	3	4

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表 7 仿真场景1中预设时间和检测结果时间对比

Table 7. Comparison of preset time and detection result time in simulation scenario 1

时间设定	预设时间	报警时间	差值/min
产生时间	08：00	08：03	+3
恢复时间	08：30	08：32	+2

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表 8 仿真场景2中预设时间和检测结果时间对比

Table 8. Comparison of preset time and detection result time in simulation scenario 2

时间设定	预设时间	报警时间	差值/min
产生时间	11：27	11：31	+4
恢复时间	11：55	11：58	+3

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表 9 仿真场景3中预设时间和检测结果时间对比

Table 9. Comparison of preset time and detection result time in simulation scenario 3

时间设定	预设时刻	报警时刻	差值/min
产生时刻	16：22	16：25	+3
恢复时刻	16：55	16：56	+1

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