CRH3与CRH5型动车组中压供电方式对比分析

介绍了CRH3与CRH5型动车组车辆在各种情况下的中压分配情况,分别分析了CRH3、CRH5型动车组的中压供电方式及其控制条件和逻辑关系,对比分析了CRH3、CRH5型动车组中压供电的优缺点。供电技术的不断提升,使CRH3、CRH5型动车组的供电更加可靠、安全和稳定,同时也推动了高速列车技术的发展,使旅客乘坐更加舒适。     

Ontologies for transportation research: A survey

Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.     

Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient.In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays.This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination.The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected.The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.     

TRIP DISTRIBUTION MODELLING USING FUZZY LOGIC AND A GENETIC ALGORITHM

This article examines possibilities for the application of soft computing techniques for the prediction of travel demand. The model, based on fuzzy logic and a genetic algorithm, successfully solves the trip distribution problem. The possibilities of using the proposed model in solving trip generation, modal split and route choice problems have also been indicated. The model has been tested on a real numerical example. Exceptionally good correspondences between estimated and real values of passenger flows have been obtained.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

基于逻辑推演理论的船舶避碰决策

提出了研究船舶避碰决策的一个新方法,并把多逻辑系统作用的结构演化引入舰艇避碰决策系统,构建了船舶避碰决策系统逻辑推理的基本理论,指出了进一步的研究方向。     

地铁列车逻辑控制电路安全服役技术应用研究

为克服传统地铁列车继电器控制电路高故障率和无记录功能等诸多缺陷,提出了基于新型二乘二取二技术的无触点逻辑控制方案。完成了系统架构设计包括热备冗余、网络通信、故障诊断、日志分析等功能。针对既有地铁列车进行了控制电路技术方案设计和施工改造,首先在分析原有控制回路原理的基础上,完成无触点逻辑控制单元替代继电器点位设计;然后综合安全性和可用性提出了故障分级方案和大旁路方案;最后在运营线路完成了列车功能验证。试验结果表明改造后列车具备逻辑完整性,功能测试符合车辆技术要求,并可实现单板故障的自动无缝切换。具备自诊断健康管理功能,在满足高可靠性高安全性的同时,实现逻辑控制的网络信息化。     

ATP/TD系统的列车位置检测原理与闭塞逻辑分析

通过对ATP/TD系统列车位置检测工作原理的描述,结合列车运行情况,观察闭塞逻辑继电器的动作及复位,分析闭塞逻辑电路的时间特性、记忆特性。     

用CPLD实现电动汽车驱动系统的逻辑控制

电动汽车电机驱动系统的控制包含各种复杂的逻辑操作,这些逻辑操作一般使用单片机或数字信号处理器等通用微处理器来实现。采用复杂可编程逻辑器件（CPLD）来实现电机驱动系统控制的各种逻辑操作,具有对事件的响应速度快、多个事件能够并行处理等优点。文章介绍了电动汽车电机驱动系统常见逻辑操作的硬件描述语言（VHDL）模型以及用CPLD来实现这些逻辑操作的具体方法。     

可编程逻辑控制器在上海轨道交通6号线设备监控系统中的应用

随着城市轨道交通的快速发展,设备监控系统越来越受到人们的关注。上海轨道交通6号线设备监控系统采用可编程逻辑控制器作为系统的核心部件。介绍了基于工业以太网构建的系统平台,分析了该系统的特点,并结合上海轨道交通6号线的具体情况,对工业以太网的实时性进行分析。