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.     

Mathematical model for optimising the sequence for clearing snow from the manoeuvring area during winter operations

This article considers the optimisation of the sequence for clearing snow from stretches of the manoeuvring area of an airport. This issue involves the optimisation of limited resources to remove snow from taxiways and runways thereby leaving them in an acceptable condition for operating aircraft. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are established during which these are open to aircraft traffic. The document contains several mathematical models each with different functions, such as the end time of the process, the sum of the end times of each stretch and gap between the estimated and the real end times. During this process, we introduce different operating restrictions on partial fulfilment of the operational targets as applied to zones of special interest, or relating to the operation of the snow‐clearing machines. The problem is solved by optimisation based on linear programming. The article gives the results of the computational tests carried out on five distinct models of the manoeuvring area, which cover increasingly complex situations and larger areas. The mathematical model is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid—Barajas Airport. Copyright © 2016 John Wiley & Sons, Ltd.

Highlights
• Optimal sequence for clearing snow from the manoeuvring area of an airport.
• Contains optimising algorithms solved using CPLEX LP‐based tree search.
• Restrictions on partial fulfilment of operational targets applied to subsets of significant stretches, used for planning the operation of snow‐clearing machines.
• Model applied to the case of the manoeuvring area of Adolfo Suárez Madrid Barajas Airport.
• Conclusions are given on the results of the computational tests carried out. There are five models of the manoeuvring area which cover increasingly complex situations and larger areas.

     

基于秩约束的车辆遮挡点恢复方法

为了恢复车辆跟踪过程中丢失的特征点,提出了一种基于秩约束的车辆遮挡点恢复方法,该方法利用所有车辆图像点组成一个秩3的矩阵,并利用该特性构造一个投影矩阵,利用该投影矩阵求到车辆遮挡点,再将求到的车辆遮挡点代替图像中的车辆遮挡点,经过多次迭代,最后求到车辆遮挡点的真实位置。该方法的优点是所有的图像及图像点都平等地看待,模拟试验和真实试验表明,该方法具有鲁棒性好、收敛性好及误差小等优点。     

粘弹性阻尼器隔震结构的振动控制研究

对设置粘弹性阻尼且基础隔震结构的混合振动控制问题进行了研究.采用开尔文模型模拟粘弹性阻尼器所产生的非线性力,推导出混合控制的运动微分方程,结合线性二次型最优控制理论（LQR）,用MATLAB编制相应的动态仿真程序进行动态仿真.研究结果表明：设置粘弹性阻尼器隔震结构采用混合控制是有效的,结构的隔震层相对位移和顶层位移反应大大降低.     

多元线性模型中的岭型预测

针对一般多元线性模型中常见的有偏预测--岭型预测与最优线性无偏预测的最优性判别进行了讨论,并就基于岭估计的最优预测量与经典预测量关于离差阵的最优性判别进行了探讨.     

测量噪声相关情况下的航迹融合和测量融合

研究了测量噪声相关情况下的航迹融合和测量融合问题,根据线性无偏最小方差估计理论,推导了噪声相关情况下的多传感器跟踪系统的航迹融合和测量融合算法.经Monte Carlo仿真表明,文中给出的考虑噪声相关的航迹融合和测量融合算法的跟踪精度均高于相应的不考虑噪声相关的传统算法,且附加的计算量很小.     

不确定性时滞系统时滞相关鲁棒镇定

讨论了具有状态时滞的数值界不确定性线性系统的时滞相关鲁棒控制问题.将矩阵分解的思想应用于线性时滞系统的控制综合,利用Lyapunov-Krasovskii泛函方法,通过牛顿-莱布尼茨各项相互关系引入"0"阵,得到了系统的一种新的经过状态反馈控制后可鲁棒镇定的、基于LMI的、保守性较低的时滞相关条件.该方法既不需要对原系统进行模型变换,也不需要对交叉项进行界定.用算例说明了该方法的有效性.     

客运企业能源消耗统计模型的研究

为了适应当前汽车运输企业的经营现状,通过建立燃油消耗统计模型,对燃油消耗量进行统计。以燃油消耗量为因变量,以影响燃油消耗的因素：周转量、客运量、车龄、驾驶员驾龄、平均车速、实栽率、行驶里程作为自变量,建立多元线性回归模型。通过拟合优度检验、F检验,判断其线性相关性,再运用逐步回归法优化统计模型,从而实现在知道有限数据信息的情况下,预测出该企业当月的车辆燃油消耗总量。将企业调研的实际统计数据代入统计模型计算的燃油消耗量,与实际统计燃油消耗量比较接近,而且统计过程得到简化。结合统计模型的研究方法开发了相应的统计软件。     

DESIGN OF A NEW INTERPOLATED CONTROLLER FOR STABILIZATION OF A SET OF INTERPOLATED PLANTS

IntroductionDynamics of almost all plants in reality variesaccording to the condition under which the plantis operated.An idea to describe the variation ofplant dynamics is interpolating a number of rep-resentative models defined at representative op-erating points.In Ref. [1 ] ,the plant was as-sumed to described as a linear interpolation ofproper stable coprime factorizations of transferfunctions of two representative models and thestabilization problems have been considered. InRef.[2 ] ,it…     

人民币对美元汇率影响因素浅析

运用简单线性回归、多自变量回归的经济计量学方法考察了人民币对美元汇率的运行结构,确立了人民币对美元汇率的多因素模型,并通过扩大样本模型检验的方法对1994年我国外汇体制改革前后的人民币对美元汇率模型进行对比分析,提出了对人民币汇率变动趋势的见解。