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.     

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Nested-Logit(ＮＬ)模型是交通方式划分的最重要的模型之一,在分析公共交通和个人交通划分方法的理论依据的基础上,根据人们在选择交通方式过程中首要考虑因素的不同,构造了不同于公共交通和个人交通的虚拟交通方式——高费用和低费用虚拟交通方式,并分析其内相关性. 在两个交通方式选择决策树结构都存在相关性和合理性的情况下,提出基于拟合优度度量比较两者的优劣,从而选定交通方式选择决策树结构. 根据实例旅客出行调查数据,利用SAS软件实现两个交通方式选择决策树结构的拟合度度量分析. 结果表明：基于拟合优度度量选定的高费用和低费用虚拟交通方式比公共交通和个人交通的虚拟交通方式更接近实际调查值,此方法具有一定的实用性.     

吊臂被动补偿装置的抗冲击性能研究

通过构建吊臂及被动补偿装置数学模型与试验研究蓄能器介入后吊臂油缸的抗冲击性能。通过实验验证得知：在油缸缩回阶段,蓄能器存储吊臂摇摆能量,缓解油缸运动冲击产生的压力波动,发挥明显的抗冲击性能;在油缸伸出阶段,蓄能器的释放油液导致油缸先加速后减速,减少泵的供油,降低系统能耗,但同时加剧了系统振动,降低了电液控制精度,根据蓄能器释能特性降低输出控制信号限制泵的供油量的方法可有效缓解超调响应,提升控制精度。     

Real-time models for wheels and tyres in an object-oriented modelling framework

This article presents models for wheels and tyres in the application field of real-time multi-body systems. For this rather broad class of applications it is difficult to foresee the right level of model complexity that is affordable in a specific simulation. Therefore we developed a tyre model that is adjustable in its degree of complexity. It consists of a list of stepwise developed sub-models, each at a higher level of complexity. These models include semi-empirical equations. The stepwise development process is also reflected in the corresponding implementation with the modelling language Modelica. The final wheel model represents a supermodel and enables users to select the right level of complexity in an unambiguous way.