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随着海事事故与海上违法行为的不断增多,智能的监控方法成为降低海事事故,打击海上违法行为的有效手段.同时,船舶自动识别系统(Automatic Identification System,AIS)的普及及船舶交通管理系统(Vessel Traffic Service,VTS)的扩建,又为智能监控提供了数据支持.鉴于此,利用船舶自动识别系统提供的数据,分析通航水域船舶信息的分布情况,根据其概率分布采用朴素贝叶斯算法,从船舶航速、航向及距航道边界距离3个方面,构建船舶异常行为检测模型.最后,以成山角通航水域为例,检验模型的有效性.实验结果表明,构建的模型能够有效地完成异常行为监测,减少海事监管人员的工作强度,同时根据实验结果分析了成山角水域船舶航行的特点,并对成山角定线制提出合理化建议.  相似文献   
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Given the enormous losses to society resulting from large truck involved crashes, a comprehensive understanding of the effects of highway geometric design features on the frequency of truck involved crashes is needed. To better predict the occurrence probabilities of large truck involved crashes and gain direction for policies and countermeasures aimed at reducing the crash frequencies, it is essential to examine truck involved crashes categorized by collision vehicle types, since passenger cars and large trucks differ in dimensions, size, weight, and operating characteristics. A data set that includes a total of 1310 highway segments with 1787 truck involved crashes for a 4-year period, from 2004 to 2007 in Tennessee is employed to examine the effects that geometric design features and other relevant attributes have on the crash frequency. Since truck involved crash counts have many zeros (often 60–90% of all values) with small sample means and two established categories, car-truck and truck-only crashes, are not independent in nature, the zero-inflated negative binomial (ZINB) models are developed under the bivariate regression framework to simultaneously address the above mentioned issues. In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation. Goodness of fit of the investigated models is evaluated using AIC, SBC statistics, the number of identified significant variables, and graphs of observed versus expected crash frequencies. The bivariate ZINB (BZINB) models have been found to have desirable distributional property to describe the relationship between the large truck involved crashes and geometric design features in terms of better goodness of fit, more precise parameter estimates, more identified significant factors, and improved predictive accuracy. The results of BZINB models indicate that the following factors are significantly related to the likelihood of truck involved crash occurrences: large truck annual average daily traffic (AADT), segment length, degree of horizontal curvature, terrain type, land use, median type, lane width, right side shoulder width, lighting condition, rutting depth (RD), and posted speed limits. Apart from that, passenger car AADT, lane number, and indicator for different speed limits are found to have statistical significant effects on the occurrences of car-truck crashes and international roughness index (IRI) is significant for the predictions of truck-only crashes.  相似文献   
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