Probabilistic Modeling of Erroneous Human Response to In-Vehicle Route Guidance Systems: A First Look

An integral component of (in-vehicle) navigation systems is the determination of optimal routes to the desired destination. An implicit assumption in the underlying algorithms is that people do not make mistakes when following the prescribed routes. This is, however, not always consistent with reality, especially when driving in unfamiliar environments. This article presents a first look at the possibility of mistakes when driving. This possibility is formalized in a Markov decision process. It is demonstrated that quite paradoxical situations can occur when accounting for mistakes. As the most interesting—but perhaps extreme—example, we have shown that under certain conditions, it is no longer optimal to recommend drivers to take the shortest route. Instead, a longer route (in certain cases even the longest!) becomes optimal. Numerical results are provided throughout the article to reveal the fundamental properties of this problem.     

Modelling heavy vehicle car‐following behaviour in congested traffic conditions

This study develops a car‐following model in which heavy vehicle behaviour is predicted separately from passenger car. Heavy vehicles have different characteristics and manoeuvrability compared with passenger cars. These differences could create problems in freeway operations and safety under congested traffic conditions (level of service E and F) particularly when there is high proportion of heavy vehicles. With increasing numbers of heavy vehicles in the traffic stream, model estimates of the traffic flow could be degrades because existing car‐following models do not differentiate between these vehicles and passenger cars. This study highlighted some of the differences in car‐following behaviour of heavy vehicle and passenger drivers and developed a model considering heavy vehicles. In this model, the local linear model tree approach was used to incorporate human perceptual imperfections into a car‐following model. Three different real world data sets from a stretch of freeway in USA were used in this study. Two of them were used for the training and testing of the model, and one of them was used for evaluation purpose. The performance of the model was compared with a number of existing car‐following models. The results showed that the model, which considers the heavy vehicle type, could predict car‐following behaviour of drivers better than the existing models. Copyright © 2013 John Wiley & Sons, Ltd.     

复制窗口不等保护喷泉码的与或树渐进性能分析

采用与或树分析方法,结合算法的构造,推导出复制窗口不等保护算法中各类数据的译码失败概率迭代公式.从数学的角度验证算法性能.通过Matlab性能仿真,给出在不同复制因数下复制窗口不等保护算法与算法误比特率性能关系曲线.通过与基于权重的不等保护算法及拓展窗口喷泉码的渐进性能仿真结果进行对比,证明了该算法在相同译码开销下,可将保护级别提高3~4个数量级.此外,本文研究了复制因数对算法性能的影响,在不同的译码开销下,通过Matlab仿真给出复制因数与算法性能的关系曲线.     

高速公路尾随相撞事件的贝叶斯网络致因分析模型

应用上海市高速公路1104条事件数据,基于专家知识和数据融合方法建立贝叶斯网络结构;利用服从Dirichlet分布的贝叶斯方法进行参数学习;运用团树传播算法进行推理分析。研究了上海市高速公路尾随相撞事件类型与不同道路环境条件之间的关系。在验证贝叶斯网络模型的有效性后,系统分析事件致因,并提出改进措施。发现重大尾随相撞事件易发生在大中型车与小型车之间;夜间易发生大中型货车的重大尾随相撞事件,尤其是凌晨0时至6时;路表潮湿状态下的非普通路段上易发生大中型客车的重大尾随相撞事件。结果表明贝叶斯网络建模能够更好的反映事件致因因素的多维性及关联性,是交通事件致因分析的有效方法。     

基于FTA的船用分油机跑油故障分析

文章基于一次船舶分油机跑油的故障处理,介绍了运用故障树分析法对分油机跑油现象的分析过程。通过故障树计算出导致分油机跑油事件的概率,并对故障概率高低进行分析,以便快速而准确地查找。     

A study on the degree of barrier coverage in wireless sensor networks

Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the difference between the barrier coverage and blanket coverage. In this paper, we propose two algorithms, namely,local tree based no-way and back (LTNWB) algorithm and sensor minimum cut sets (SMCS) algorithm, for the opened and closed belt regions to determine the degree of the barrier coverage of WSN. Our main objective is to minimize the complexity of these algorithms. For the opened belt region, both algorithms work well, and for the closed belt region, they will still come into existence while some restricted conditions are taken into consideration.Finally, the simulation results demonstrate the feasibility of the proposed algorithms.     

基于Double—Bagging决策树的基因微阵列数据研究

Bagging通过组合不稳定的分类器在很大程度上降低了"弱"学习算法的分类误差.基于Torsten等人提出的Double-Bagging算法.本文对其加以修改并应用于基因微阵列数据的处理.在给定的训练数据集和测试集上试验并比较了多种分类器,结果表明Double-Bassing决策树分类精确度优于Bagging决策树和C4.5算法.     

改进的属性约简算法及其在决策树中的应用

针对传统的基于区分矩阵属性约简算法在构建区分矩阵时生成很多冗余元素,空间和时间性能上不够理想的缺点,利用动态数组及新的约简过程进一步改进,最后把新的属性约简算法应用到决策树的构建上,实验证明,该算法能够提高决策树分类性能.     

基于最优加权Steiner树的枢纽型物流中心选址问题

为了满足近年来物流运输业快速发展的需要,促进物流中转运输网络的合理化建设,研究了枢纽型物流中心的功能和选址原则,详细分析了影响枢纽型物流中心选址的各种因素,提出了基于结点带权的欧氏Steiner最优树的枢纽型物流中心选址方案.针对该方案设计了相应的智能优化算法,并进行了具体的程序实现.借助该方案不仅可以使总的运输成本最小,而且能够在无需事先确定备选点的数量和位置的情况下实现同时确定枢纽型物流中心的数量及位置的目标.最后以长三角地区枢纽型物流中心的建设问题为背景,对各种数据进行了仔细的分析比较,从中确定若干区域作为物流服务需求点集,并将各种因素的综合效用作为物流需求点的权值,对上述算法进行了有效性验证.     

铁路工务安全状态预警决策支持系统构建研究

构建以"工务安全"为主题的预警决策支持系统是目前铁路安全管理发展的一个趋势,总结广深铁路工务安全状态预警决策支持系统建设经验,基于"以监测数据客观反映生产安全,以风险分析和安全评价方法指导安全管理,实现铁路工务安全状态预警决策支持目标"建设思想,通过基于数据仓库的工务安全信息集成、决策报表分析、数据挖掘分析和专家知识库预警等技术,提出构建铁路工务安全状态预警决策支持系统新方法.