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。
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 accura cy 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.
作者
吴建华
彭虎
王辰
付鹏
WU Jianhua;PENG Hu;WANG Chen;FU Peng(School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China;Transport Planning and Research Institute,Ministry of Transport,Beijing 10020,China;China Communications Planning&Design Institute for Water Transportation,Beijing 100007,China)
出处
《交通信息与安全》
CSCD
北大核心
2023年第5期83-94,共12页
Journal of Transport Information and Safety
基金
国家自然科学基金项目(52271366)资助。
关键词
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
traffic safety
maritime traffic accident
maritime traffic accident detection method
Kalman filter
AIS communication volume
