航道内实时船舶交通流航行风险主动评估  

Proactive Evaluation on Sailing Risk of Real-time Ship Traffic in Waterway

作　　者：张树奎[1,2] 肖英杰[2] 苏文明[1] 

机构地区：[1]江苏海事职业技术学院航海学院,江苏南京211170 [2]上海海事大学商船学院,上海201306

出　　处：《重庆交通大学学报（自然科学版）》2016年第2期151-155,共5页Journal of Chongqing Jiaotong University(Natural Science)

基　　金：交通运输职业教育科研项目(2013A03);中国交通教育研究会课题(20140233)

摘　　要：为主动评估航道内实时交通流航行风险,并对交通事故状态做出预警,利用高斯混合模型和最大期望算法构建了贝叶斯网络。基于长江草鞋峡水道内船舶检测器数据和交通事故数据,学习和训练贝叶斯网络的结构和参数,构建了贝叶斯网络分配器。分别对8组船舶交通流数据建立贝叶斯网络分类模型,结果表明：采用交通事故发生前20～40 min内、且距离事故地点最近的2个船舶检测器的数据构建的模型分类效果最优,正确率为78.13%。最后通过与BP神经网络和K近邻两种估计算法比较,证明了BN模型预测效果更优,是一种较好的实时交通流航行风险评估方法。In order to assess sailing risk of real-time ship traffic in waterway and give early warning of accident,the Bayesian network was built by using Gaussian mixture model and maximum expectation algorithm. Through our study and training about Bayesian network structure and parameters and based on the data from ship detectors and the record of traffic accident occurred in Caoxiexia waterway of Yangtze River,BN distributor was established. BN classification models in relation to 8group data of ship traffic flow were set up respectively. Results show that the model using data taken from the two ship detectors closest to traffic scene within 20 to 40 minutes just before occurrence of accident generates optimum prediction results with accuracy up to 78. 13%. The result of comparison among BN,BP neural network and K close neighbor proves that BN prediction model generates a more accurate prediction result and is thus a good method of risk evaluation of sailing ships in real-time.

关 键 词：交通工程 航道 交通事故 贝叶斯网络 风险 主动评估 

分 类 号：U695.2[交通运输工程—港口、海岸及近海工程]