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%。     

城市无信号控制T型交叉口通行能力计算方法

以当量人群描述非机动车和行人对机动车通行的共同影响,对城市无信号控制T型交叉口的交通流运行优先等级进行重新划分,共划分为5 级.将主路直行车流和横穿支路的当量人群流作为独立优先流,应用间隙接受理论,研究了各次级交通流的可能通行能力计算方法.考虑高等级次级交通流及横穿主路的当量人群流的影响,采用概率论方法研究了各次级交通流的可能通行能力修正系数,从而得到各次级交通流的实际通行能力计算模型,进而得到整个无信号控制T型交叉口的通行能力计算方法.结果表明,以当量人群描述非机动车和行人对机动车通行的共同影响计算过程简单,符合我国城市道路交叉口非机动车和行人多的实际情况.     

直升机双机协同飞行投雷研究

基于舰载直升机单机飞行投雷的基础上,通过对双机协同飞行投放鱼雷方法、双机协同组织过程的分析,建立了攻击机投放参数的解算模型,通过仿真计算,证明双机协同飞行投放鱼雷能有效地提高鱼雷的命中概率。     

直航鱼雷等概率阵位图

在考虑各观测量误差的情形下,给出了计算直航鱼雷命中概率的解析公式、等命中概率曲线的解析公式及一定命中概率条件下射距最远的阵位的求法,可为鱼雷作战使用提供理论上的依据。