Modelling demand in restricted parking zones

Multiple linear regression (MLR) and geographically weighted regression (GWR) models are used for estimating parking demand in areas with paid short stay parking systems. These models have been applied to the city of Santander (Cantabria, Spain) to check their goodness of fit and their predictive ability. The results show the main advantages and disadvantages of using GWR models. The technique proved to be useful in this case study because it offered a better fit and made better predictions in a scenario showing a certain degree of spatial heterogeneity unexplained by any of the variables introduced into the global model. However, the GWR model also presented situations of local correlation although this was considered moderate given the results provided by the variance inflation factors and the local condition indexes.     

Analysis of peak and non‐peak traffic forecasts using combined models

Accurate and timely traffic forecasting is crucial to effective management of intelligent transportation systems (ITS). To predict travel time index (TTI) data, we select six baseline individual predictors as basic combination components. Applying the one‐step‐ahead out‐of‐sample forecasts, the paper proposes several linear combined forecasting techniques. States of traffic situations are classified into peak and non‐peak periods. Based on detailed data analyses, some practical guidance and comments are given in what situation a combined model is better than an individual model or other types of combined models. Indicating which model is more appropriate in each state, persuasive comparisons demonstrate that the combined procedures can significantly reduce forecast error rates. It reveals that the approaches are practically promising in the field. To the best of our knowledge, it is the first time to systematically investigate these approaches in peak and non‐peak traffic forecasts. The studies can provide a reference for optimal forecasting model selection in each period. Copyright © 2010 John Wiley & Sons, Ltd.     

The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models

The likelihood functions of multinomial probit (MNP)-based choice models entail the evaluation of analytically-intractable integrals. As a result, such models are usually estimated using maximum simulated likelihood (MSL) techniques. Unfortunately, for many practical situations, the computational cost to ensure good asymptotic MSL estimator properties can be prohibitive and practically infeasible as the number of dimensions of integration rises. In this paper, we introduce a maximum approximate composite marginal likelihood (MACML) estimation approach for MNP models that can be applied using simple optimization software for likelihood estimation. It also represents a conceptually and pedagogically simpler procedure relative to simulation techniques, and has the advantage of substantial computational time efficiency relative to the MSL approach. The paper provides a “blueprint” for the MACML estimation for a wide variety of MNP models.     

Sight distance analysis of highways using GIS tools

Analyzing the distance visible to a driver on the highway is important for traffic safety, especially in maneuvers such as emergency stops, when passing another vehicle or when vehicles cross at intersections. This analysis is necessary not only in the design phase of highways, but also when they are in service. For its use in this last phase, a procedure supported by a Geographic Information System (GIS) has been implemented that determines the highway distances visible to the driver. The use of a GIS allows the sight distance analysis to be integrated with other analyses related to traffic safety, such as crash and design consistency analyses. In this way, more complete analyses could be made and costs shared. Additionally, with the procedure proposed it is possible to use data regarding the trajectory of a vehicle obtained on a highway with a Global Positioning System (GPS) device. This application is very useful when highway design data are not available. The procedure developed and its application in a case study are presented in this article.     

基于多层隐类贝叶斯网络的舰船生存能力评估模型研究

生存能力是舰船设计时需要考虑的一个重要性能指标,然而其涉及的因素较多,因素之间的关系错综复杂,不确定信息充斥其间.在系统分析舰船生存能力评估要素的基础上,针对评估过程中的不确定性信息难以量化处理的特点,引入基于贝叶斯网络的多层隐类模型算法对舰船生存能力进行评估,给出了模型评分原理.最后以实例说明了建模方法与评估过程,并结合专家意见分析了模型的优劣,说明该多层隐类模型的算法符合实际情况.     

预燃室催化燃烧对HCCI发动机燃烧特性的影响

对预燃室壁涂有催化剂的均质压燃(HCCI)发动机的燃烧过程进行了数值计算,分析了催化燃烧对HCCI发动机燃烧特性的影响;同时分析了预燃室内催化剂种类、过量空气系数、进气温度、进气速度、缸径以及预燃室壁温对HCCI发动机燃烧特性的影响。结果表明,预燃室存在催化燃烧时对HCCI发动机的着火时刻有很大的影响:随着过量空气系数及预燃室进气速度的增加,HCCI发动机的着火时刻提前;催化剂种类、预燃室缸径以及预燃室壁温对HCCI发动机着火时刻影响不显著,但对缸内燃烧温度影响显著。     

基于自然驾驶数据的高速公路跟驰模型参数标定

为了研究中国驾驶人在高速公路上的跟驰行为特征,从上海自然驾驶研究试验数据库中提取48位驾驶人在高速公路上的跟驰事件并进行特征分析。利用自动化筛选准则及人工验证方式提取1 548个有效事件,选取后车车速与车头间距为性能指标,其均方根百分比误差之和为目标函数,利用遗传算法对Gazis-Herman-Rothery模型、GIPPS模型、智能驾驶人模型、全速度差模型和Wiedemann模型进行参数标定及效果验证。基于误差、碰撞及后退等异常情况出现次数等比较其表现性。研究结果表明：不同模型对中国驾驶人的适应性不同,智能驾驶人模型具有最小的误差和误差标准差,更加适合仿真中国驾驶人在高速公路上的跟驰行为。研究结果对于开发适合于中国驾驶人与道路环境特征的跟驰模型具有重要价值。     

Bi-fidelity Kriging model for reliability analysis of the ultimate strength of stiffened panels

A method based on a Bi-fidelity Kriging model is proposed for structural reliability analysis. It is based on adding low-fidelity data samples to the model to predict high-fidelity values, thus saving computational effort. Distance Correlation develops the correlation between the low and high-fidelity functions, initially proposed to assess the correlation between two variables. The bi-fidelity Kriging response surface model's efficiency as a surrogate model will be assessed for structural reliability problems that demand high computational costs, such as nonlinear finite element analysis structural models. The efficiency assessment is performed by comparing the accuracy of the failure probability predictions based on the Subset Simulation and First-order reliability method using the Bi-fidelity Kriging model as a surrogate for the performance function. The idea is illustrated by considering a representative component of marine structures analyzed by finite element analysis to create bi-fidelity scenarios to assess structural reliability with many variables. The results show that the proposed multi-fidelity method can provide an accurate failure probability estimation with less computational cost.     

Uncertainty analysis of extreme mooring loads associated with environmental contours and peak tension distributions

This paper aims to assess the uncertainty on the extreme mooring loads of floating system considering short-term variability. Two environmental contour approaches based on the inverse First and Second Order Reliability Methods are employed to identify critical sea states that may give rise to extreme loads. The uncertainty related to the construction of environmental contours is addressed including significant differences due to marginal distribution fitting, parameter estimation methods and joint models. Three measured datasets are analysed using a known conditional joint distribution and proposed mixed copula model. 3-h time domain numerical simulation for each sea state is conducted and the characteristic extreme responses of mooring lines subjected to design loads are assessed. The uncertainties due to various statistical models including the average conditional exceedance rate method as well as global maxima, peak-over-threshold method combined with Gumbel distribution, Generalized Extreme Value distribution, Generalized Pareto distribution and 3-parameter Weibull distribution are investigated and quantified. It is observed that marginal distributions, joint models and parameters estimation methods have apparent effect on design loads estimation, and the extreme tensions of the semi-submersible platform shows significant difference using various probabilistic models. The results indicate that those epistemic uncertainties should be account for in the reliability analysis or safety factor calibration for mooring systems.     

基于OPC UA技术的T型梁焊接机器人在线监测系统

针对薄板分段车间的焊接装备复杂化程度高、焊接作业监控困难、焊接信息反馈不及时等问题，研究薄板分段车间T型梁焊接机器人的工艺流程和工艺参数，开发一种基于对象链接与嵌入过程控制的统一架构[OLE（Object Linking and Embedding）for Process Control Unified Architecture，OPC UA]技术的T型梁焊接机器人在线监测系统。该系统主要对T型梁焊接机器人的生产进度、设备运行状态和中间产品质量等进行在线监测，实现数据实时采集和生产过程监控与分析等功能，可消除设备之间的信息孤岛现象，实现车间生产透明化，提高生产效率，确保焊接质量。