Network traffic control based on a mesoscopic dynamic flow model

The paper focuses on Network Traffic Control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

铁路信号集中监测系统中心网络

主要阐述了铁路信号集中监测系统中心网络的体系结构、动态路由协议和信号集中监测系统中心网络安全的重要性。     

基于模糊神经网络的驼峰推送速度控制研究

对影响驼峰机车推送速度的各种因素进行了探讨,将模糊神经网络理论引入到驼峰变速推峰系统中,结合神经网络和模糊控制的各自优点,将专家或熟练操作人员的控制策略转换为控制功能函数,利用神经网络自学习、自校正的能力调整、修改模糊控制规则,从而实现对推送速度的优化控制。     

基于网络安全的大秦线网络优化方案设想与实施

对大秦线铁路通信中应用的GSM-R网络的安全性能以及安全防范措施进行全面叙述;对网络优化从基本概念、基本理论和现场实践等诸多方面予以阐述;就大秦线网络优化方案提出设想并从理论和实践两个方面进行论证和实施总结。     

TDCS/CTC系统网络故障原因分析与排除

TDCS/CTC系统发生网络故障较多,故障种类也繁杂,如果把常见的典型故障进行归类总结,对迅速准确的查找故障根源、压缩故障处理延时很有益处。根据多年来对TDCS/CTC网络的维护经验,阐述了TDCS/CTC网络站内及站间的网络结构,并列举了常见的TDCS/CTC网络故障,对故障现象及解决方法进行了详细说明。     

铁路信息网络准入控制方案研究

使用网络准入控制技术对接入终端进行身份认证、安全检查、授权访问,只允许合法的、值得信任的终端接入网络。结合铁路信息网络现状进行研究,提出一种铁路信息网络准入控制方案,从而提高网络的可用性和安全性。     

线网时代轨道交通TETRA系统如何应对

基于城市轨道交通线网建设的需求,分析现有各个城市轨道交通网络建设过程中TET-RA系统互联互通的案例,总结归纳出6种TETRA系统互联互通的方式,对其优缺点进行了论述及比较.     

面向控制网络协议安全的隔离平台设计与实现

为提高铁路信号控制系统的安全性,设计一种基于UCOS-Ⅱ面向控制网络安全协议的隔离平台。提出DUAL-MCU(LPC3250)+DUAL-PORT STATIC RAM的系统构架,集成了以太网、CAN、PROFIBUS、422及232等网络协议标准和现场总线技术,对控制网络数据进行综合监控与过滤,提高了网络数据安全性。     

GSM-R网络管理与优化专家系统的设计与实现

GSM-R网络的性能和服务质量对保障铁路安全有序运营有重要意义.本GSM-R网络管理与优化专家系统是一种综合分析平台,该平台能够结合接口监测信令、OMC网管数据等多种数据源进行综合关联分析,具备数据查询、图形化显示、专家决策推理等功能,同时系统引入人工智能领域专家系统的设计方法,建立合理的网络优化知识库,运用有效的优化推理机制,模拟网优专家的决策过程,针对网络服务质量问题进行推理分析,辅助解决GSM-R网络优化领域的相关问题.