基于条件熵约简和粗糙集规则匹配的反辐射无人机作战目标威胁识别

文章采用粗糙集的方法来处理威胁识别中的不确定、未知信息,利用条件熵度量威胁识别属性的重要程度,并提出了一种新的规则匹配方法识别威胁等级。通过实例分析与对比,证明此方法能够为反辐射无人机作战目标威胁等级的判断提供了一种可行的途径。     

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

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突发事件一旦发生,应急物资需求种类是纷繁复杂的,应急物流中的物资需求又是紧迫的、动态变化的. 为了提高应急物流的运作效率,救援管理人员需要对应急物资进行分级. 本文提出一种基于后验概率支持向量机的应急物资分类方法,并在此基础上建立应急物资分类模型. 把物资的重要性、时效性和缺口程度三个因素作为物资需求分级的评价标准,将模糊化后的评价值作为PPSVM的输入,选择后验概率最大所的类别作为测试样本的类别输出来完成整个分类过程,从而得到对应的分级结果. 实验结果表明,PPSVM用于应急物资分类是可行的,能够很好地解决应急物资的分类问题.     

城市轨道交通断面客流不确定性分析的广义自回归条件异方差改进模型

对比分析指出,城市轨道交通线路断面客流量变化与道路交通断面客流量变化具有相似性,但城市轨道交通线路断面客流时间序列具备特有的尖峰厚尾特性,其变化的敏感程度依赖时空条件,常用于道路领域的广义自回归条件异方差(GARCH)模型无法直接用于城市轨道交通领域。基于此,引入广义误差分布(GED)虚变量,构建改进的GARCH模型,并基于成都地铁1号线下行断面客流时间序列数据,借助EViews和Matlab软件对改进前后的模型效果进行实证对比分析。结果表明,改进后的虚变量GARCH模型比原始的GARCH模型具有更好的适用性。     

基于CVaR的海运集装箱超订模型

将条件风险价值(CVaR)度量准则应用于集装箱舱位超订的风险管理研究.建立了在CVaR准则下的海运集装箱舱位超订模型,解此模型得到舱位最优超订水平需满足的方程组,并讨论了风险厌恶程度和空箱调运对舱位最优超订水平的影响.分析结果表明,在风险厌恶环境中,集装箱舱位的最优超订水平依赖于需求的分布和风险厌恶程度,不一定小于风险中性时的最优超订水平;与不考虑空箱调运时比较,考虑空箱调运时,最优超订水平会降低.     

拟概率与条件拟概率

讨论拟概率的基本性质，引入拟概率的Ｆｕｚｚｙ积分和条件拟概率等定义，并在此基础上建立拟概率的Ｒａｄｏｎ－Ｎｉｋｏｄｙｍ定理。     

考虑偏好差异的后疫情时代居民出行方式选择行为研究

为探究后疫情时代居民出行方式选择行为，运用选择实验的方法，基于问卷调查获得选择行为数据，构建出行方式选择的混合Logit模型和潜在类别条件Logit模型。采用Stata软件标定模型参数，得到后疫情时代影响居民出行方式选择的主要因素。结果表明，两种模型均体现了个体出行方式选择的异质性，潜在类别条件Logit模型与混合Logit模型相比拟合优度提高了13%， 预测精度提高了3.03%，为突发公共卫生事件下分析出行行为的个体异质性提供了一种有效工具。潜在类别条件Logit模型根据居民所处低、中风险区两种情景，分别将居民划分为4类、5类人 群。从出行方式属性上看，等待时间和在途时间成为居民选择出行方式最重要的影响因素。从个人社会经济属性上看，在后疫情时代收入更高的女性更倾向选择私家车出行，年龄越大对行程费用越敏感，男性更愿意选择公交、地铁出行。     

Cantor型随机变量序列强极限定理的研究

研究了对任意Cantor型随机变量序列普遍成立的一类强大数定理。证明中利用条件期望的概念,采用测度的网微分法并运用纯分析运算得出结论,由推论得到随机变量序列已有的经典强大数定律以及对任意随机变量序列普遍成立的强大数定律。     

任意随机序列关于随机选择系统的一类强极限定理

主要研究任意随机序列在随机选择系统中的随机条件概率其调和平均的强极限定理。在证明中采用了一种把网微分法与条件矩母函数相结合应用于随机选择系统强极限定理研究的一种途径。作为推论,得到m重非齐次马氏链的一个强极限定理,并将已有的结果加以推广。