Accommodating risk in the valuation of expected travel time savings

Valuation of travel time savings is a critical measure in transport infrastructure appraisal, traffic modelling and network performance. It has been recognised for some time that the travel times associated with repeated trips are subject to variation, and hence there is risk embedded in the treatment of expected travel time. In the context of the expected utility framework, we use a nonlinear probability weighting function to accommodate choice made under risk. Although the empirical findings suggest small differences between the value of expected travel time savings (VETTS) in the presence and absence of risk, the mean estimate does make a noticeable difference to time benefits when applied to real projects. By incorporating nonlinear probability weighting, our model reveals that the probabilities associated with specific travel times that are shown to respondents in the choice experiment are transformed, resulting in overweighting of outcomes with low probabilities and underweighting of outcomes with high probabilities. Copyright © 2011 John Wiley & Sons, Ltd.     

基于破坏概率分析的滑坡稳定性评价

结合三峡库区某滑坡的治理工程,利用Slope/W软件将破坏概率分析对滑坡的稳定性进行评价。研究结果表明:基于破坏概率分析的滑坡稳定性评价,可以降低由于抗剪强度参数的随机性、测试的误差、地质体的不均匀性、外界因素变化的随机性等对于滑坡稳定性评价结果的影响,能较为客观地反映岩性的实际分布状况。相比单一稳定性系数评价滑坡稳定性的方法,将滑坡破坏概率与传统的稳定性系数相结合,可较客观地评价滑坡的实际稳定状态。分析结果表明:在滑坡含水饱和+地震条件下,该滑坡处于破坏状态。     

苏通大桥斜拉桥伸缩缝位移的长期监测与分析

以苏通大桥斜拉桥347 d的伸缩缝位移为对象,研究苏通大桥斜拉桥伸缩缝服役性能评价方法.首先提出了伸缩缝位移的概率统计分析方法,在此基础上验证了伸缩缝设计性能.其次,提出了伸缩缝位移与温度的相关性分析方法,在此基础上评价了伸缩缝使用性能.分析结果表明:①苏通大桥斜拉桥的伸缩缝位移可以通过2个正态分布函数的加权和来描述其概率分布,温度年变化作用下伸缩缝的极值位移远小于伸缩缝设计位移;②苏通大桥斜拉桥伸缩缝位移与温度具有良好的线性相关性,并且苏通大桥北端的伸缩缝存在性能退化的可能,需要积累更多的数据进行分析.     

Strength reliability analysis of stiffened cylindrical shells considering failure correlation

The stiffened cylindrical shell is commonly used for thepressure hull of submersibles and the legs of offshore platforms.There are various failure modes because of uncertainty with thestructural size and material properties, uncertainty of the calculationmodel and machining errors. Correlations among failure modesmust be considered with the structural reliability of stiffenedcylindrical shells. However, the traditional method cannot considerthe correlations effectively. The aim of this study is to present amethod of reliability analysis for stiffened cylindrical shells whichconsiders the correlations among failure modes. Firstly, the jointfailure probability calculation formula of two related failure modesis derived through use of the 2D joint probability density function.Secondly, the full probability formula of the tandem structuralsystem is given with consideration to the correlations among failuremodes. At last, the accuracy of the system reliability calculation isverified through use of the Monte Carlo simulation. Result of theanalysis shows the failure probability of stiffened cylindrical shellscan be gained through adding the failure probability of each mode.     

A hybrid Bayesian Network approach to detect driver cognitive distraction

Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.     

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。     

基于支持向量机的大跨连续刚构桥可靠度分析

针对大跨连续刚构桥在可靠性分析过程中功能函数不能显示、表达的问题,提出了一种基于支持向量机分类功能可靠度的计算方法。采用拉丁超立方抽样产生一定样本的数据库,通过改变样本点中效应数据的大小,并结合有限元程序的分析,找出一定精度下离分类面最近的一对样本点,从而得到新的样本库。然后,用新的样本库构造出一个 SVM分类器函数。结合蒙特卡罗数值模拟,计算其失效概率。以四川某大跨连续刚构桥为例,验证了该方法的实用性。     

外部交通区划分方法的改进研究

外部交通区的划分是资料收集和需求预测的基础。分析了运输需求预测中常见的外部交通区划分方法,找出了其存在的问题。建立了对外运输需求的概率分布模型,提出了改进的外部交通区划分方法。并基于改进的外部交通区划分,提出了对外运输需求方式分担预测的思路和步骤。最后,结合河南省驻马店市综合客运需求现状对该方法进行了验证。结果表明：改进的外部交通区划分方法能在不进行OD调查的前提下显著降低方式分担预测的误差,有很强的工程应用价值。     

无信号控制平面交叉口交通冲突预测模型研究

以无信号控制平面交叉口为研究对象,在交通流运行特征分析的基础上,用泊松分布描述车辆的到达特性。通过相关假定,指出机动车与机动车之间冲突发生的条件及冲突数的计算方法,认为平交口内车辆之间的冲突是按照一定概率同时进入交叉口的车辆相互干扰的结果。运用概率统计的相关理论计算出在车辆横穿交叉口的时间CT内,单进口道发生的冲突数,进而计算出单进口道的小时冲突数及整个交叉口的小时冲突数。建立了冲突预测模型,并利用实测数据对模型进行了验证。     

贝叶斯假设方法在水声成像声呐阵控制中的应用

在对水中目标轮廓进行水声成像时,可设计声呐阵中各声呐单体对目标分割识别,利用贝叶斯假设方法,解决多声呐单体识别结果的目标最终识别问题,实现声呐阵的有效控制。