车辆参数随机的车桥竖向随机振动分析

车辆参数的随机性对车桥耦合随机振动的影响不可忽略。基于车-桥竖向耦合模型,考虑车辆荷载及一系、二系竖向弹簧刚度与弹簧阻尼的随机性,采用数论选点法对多维随机变量选取离散代表点,并基于正态分布利用Voronoi区域对代表点集进行剖分赋得初始概率;建立概率密度演化随机动力方程,基于MATLAB编制车桥耦合随机振动分析程序;采用newmark-β积分法及带TVD格式的双边差分法计算车桥振动响应均值、标准差及时变概率密度演化分布。结果表明:与随机模拟法相比,概率密度演化法分析车桥随机振动精度更高,计算效率提高1~2个数量级;车辆载重随机荷载是车桥竖向耦合随机响应的主因之一;不可忽略二系竖向弹簧随机刚度及随机阻尼对列车竖向振动的影响;桥梁挠度均值整体随车速先增大后减小,其变异系数受车速的影响。     

适合于感知无线电网络的动态信道分配和注水功率联合的算法

随着无线通信技术的飞速发展,传统的静态频谱分配机制与频谱实际需求之间的矛盾日益显著,频谱短缺现象日益严重.频谱资源的利用率问题成为未来移动通信发展的一大瓶颈.有效的信道分配机制能够改善无线资源的使用效率.本文将注水功率分配算法和动态信道分配算法相结合,提出一种基于小区的分布式动态信道分配算法.此算法针对感知无线电网络频谱变化的环境和多业务的环境能够提供优越的性能和自适应能力.与其他基于小区的动态信道分配算法相比,此算法考虑用户之间的公平性,显著提高频谱使用效率.     

30000 DWT载货教学实习船破舱稳性研究

30000 DWT载货教学实习船是上海船舶研究设计院为大连海事大学专门开发设计的无限航区多用途船兼教学实习船。作为教学实习船,保障实习生、教师和船员的生命安全至关重要,而破舱稳性是考核该船是否足够安全的重要性能指标。分析了2008年《特种用途船安全规则》及SOLAS 2014对特种用途船和普通干货船的概率破舱稳性计算要求的区别,通过采取设计U型压载水舱和特殊布置管系及阀等措施,最终得到了较小的破舱稳性GM限制,提高了货物装运能力。     

Modeling delay at signalized intersections with channelized right‐turn lanes considering the impact of blockage

This paper presents a probabilistic delay model for signalized intersections with right‐turn channelization lanes considering the possibility of blockage. Right‐turn channelization is used to improve the capacity and to reduce delay at busy intersections with a lot of right‐turns. However, under heavy traffic conditions the through vehicles will likely block the channelization entrance that accrues delay to right‐turn vehicles. If the right‐turn channelization gets blocked frequently, its advantage in reducing the intersection delay is neglected and as a result the channelization lane becomes inefficient and redundant. The Highway Capacity Manual (HCM) neglects the blockage effect, which may be a reason for low efficiency during peak hours. More importantly, using HCM or other standard traffic control methods without considering the blockage effects would lead to underestimation of the delay. To overcome this issue, the authors proposed delay models by taking into account both deterministic and random aspects of vehicles arrival patterns at signalized intersections. The proposed delay model was validated through VISSIM, a microscopic simulation model. The results showed that the proposed model is very precise and accurately estimates the delay. In addition, it was found that the length of short‐lane section and proportion of right‐turn and through traffic significantly influence the approach delay. For operational purposes, the authors provided a step‐by‐step delay calculation process and presented approach delay estimates for different sets of traffic volumes, signal settings, and short‐lane section lengths. The delay estimates would be useful in evaluating adequacy of the current lengths, identifying the options of extending the short‐lane section length, or changing signal timing to reduce the likelihood of blockage. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.     

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.     

贝叶斯网络在电控发动机燃油系统故障分析中的应用

贝叶斯网络(Bayesian Network)是近年来发展起来的一种基于概率理论的推理工具,本研究简要介绍了贝叶斯网络的基本理论,建立了电控发动机燃油供给系统故障信息的贝叶斯诊断网络,并利用Hugin Expert软件计算当燃油供给系统某一部件出现故障时不同故障现象出现的概率。研究表明,贝叶斯诊断网络能够有效诊断电控发动机燃油供给系统故障信息关联度。     

水泥砼路面机械化施工系统理论应用研究(Ⅰ)——系统数学描述

根据高等级公路水泥砼路面机械化施工工艺,利用随机服务系统理论,提出了水泥砼路面机械化施工系统的数学模型,动态地描述了水泥砼路面机械化施工系统机械运行的一般规律．     

基于FTA-BN的综合交通枢纽中地铁车站安全风险评价

以综合交通枢纽中的地铁车站安全为研究目标,综合考虑综合交通枢纽中特殊的客流组成和乘客特征,以踩踏、火灾、水灾、公共卫生和大面积滞留5类易发风险事故作为研究对象,基于FTA-BN方法对其影响因素进行分析,识别其风险因素,建立相应的事故树模型,转化为贝叶斯网络模型进行风险评价;引入三角模糊数处理专家自然语言,得到贝叶斯网络中的先验概率和条件概率分布,然后通过贝叶斯网络模型进行网络推理计算和敏感性分析,找出地铁车站中的薄弱部分,制定相应的风险管控措施,从而提高枢纽中地铁车站对于紧急事件的应对能力。以广州南站地铁车站为例进行快速评价,结果表明：广州南站在公共卫生安全方面存在一定危险,发生概率为42.98%,且较易发生大面积滞留事件,可能性为30.40%。