Rhinoceros软件在客车造型设计中的应用

介绍造型设计软件Rhinoceros,并以豪华大客车为例,讲述该软件在客车CAS(计算机辅助造型设计)中的应用。     

面向驾驶模拟器的高等级公路三维视景建模与实现

随着汽车驾驶模拟器在道路交通仿真中的广泛应用,驾驶员对视景的逼真度也相应地提出了较高的要求。视景仿真系统作为模拟器的一个子系统,其逼真度和实时的动态显示是影响道路交通仿真的关键因素。文中介绍了高等级公路三维视景的建模过程、实现方法,以及地形的简化技术。设计成果表明,这种方法对于利用驾驶模拟器进行道路交通仿真等方面的研究是可行的。     

行人头部与车辆碰撞中旋转速度对颅脑组织响应影响

为了探究旋转速度在行人头部与车辆碰撞中对颅脑组织响应影响,对46例真实交通事故进行了运动学重建获得行人头部与车辆碰撞线性、旋转速度分布特征,并依此建立了头部与风挡、发动机罩碰撞有限元仿真模型,分析了头部旋转速度在不同线性速度和碰撞位置下对颅脑应力应变响应影响特征.结果表明:在行人头部与风挡碰撞中,当线性速度小于40 k...     

基于Simulink的单相矩阵变换器的研究

介绍了矩阵变换器(MC)的优点,在此基础上分析单相矩阵变换器的工作原理、拓扑结构及推导过程,并基于Simulink进行仿真研究.以此来证明这种变换器的优点.通过仿真结果,发现这种交-交变换器具有输出频率连续可调、输出电压波形存在畸变,但在一定范围内连续可调、输入输出电流较接近正弦波等优点,发展前景大,值得进一步研究.     

污水管网系统节点流量模拟计算

污水管网系统节点流量模拟计算是城市污水管网系统规划设计和运行管理的基础性工作。该文从污水管网系统规划设计和运行管理两个方面阐述了污水管网系统节点流量模拟计算的内涵,并对该领域的未来发展作了展望。     

考虑驾驶人风格的跟车预警规则研究

为研究驾驶人的跟车特性及探究可适用于不同风格驾驶人的跟车预警规则,为自动驾驶车辆开发可满足不同用户驾驶需求和驾乘体验的主动安全预警系统,选取50名被试驾驶人开展实车试验,采集驾驶人跟车行为表征参数并基于雷达数据确定跟车事件提取规则。选取平均跟车时距和平均制动时距为二维向量,使用基于K-means聚类结果的高斯混合模型将驾驶人聚类为3种风格类型(冒进型、平稳型、保守型)。通过分析3组驾驶人的跟车及制动数据,将不同类型驾驶人的制动时距分位数作为跟车预警阈值,结合实际预警数据及不同制动时距分位数对应的预警正确率,对现有跟车预警规则进行调整,以适应不同类型驾驶人的驾驶需求。研究结果表明:3组驾驶人的平均跟车时距和平均制动时距差异显著,冒进型驾驶人倾向于选择较小的跟车时距和制动时距,保守型驾驶人的跟车时距和制动时距则普遍较大;3组驾驶人的实际跟车预警次数为215次,驾驶人采取制动操作而系统未予以预警的次数为329次,系统整体预警正确率为21.9%,漏警率为87.5%,通过分析信息熵等判定当前预警规则并不合理;将每类驾驶人制动时距的10%分位数作为阈值时的预警效果较好,调整后的跟车预警规则能在一定程度上适应不同的驾驶人类型。     

马尔柯夫灰色残差模型在剐构桥控制中的应用

文章结合罗天乐特大桥的马尔柯夫灰色残差模型预测实例,介绍马尔柯夫灰色残差模型在线性控制中的原理、计算及应用过程,并根据实际监控经验提出了建议。罗天乐特大桥的具体监控应用实践表明,马尔柯夫灰色残差模型预测精度高,能较好地应用于大跨连续刚构桥的施工线形控制。     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

A model for the visualization and analysis of elevator traffic

ABSTRACT

Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.     

Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.