紧邻建筑物大断面浅埋暗挖地铁隧道施工控制技术

北京地铁十号线一期终点折返线由于地处东三环,周边环境比较复杂,针对浅埋暗挖法施工过程中大断面折返线不同断面的开挖顺序以及楼房的防护措施进行模拟分析,优化确定合理的楼房保护措施和开挖方案,为工程顺利建设提供技术支持,并对以后类似环境条件下工程积累经验。     

城市轨道交通换乘站综合监控平台的构建

介绍了车站级综合监控平台及其功能。整合了换乘站各弱电系统,给出了弱电系统接入综合监控平台的方式。在此基础上,针对全新2线换乘站提出了车站综合监控平台构建的三种方案,并对其进行了对比分析,确定了三种方案的适用情况。     

临近既有线的地道软土深基坑支护技术

庄桥站旅客地道为临近既有线软土深基坑工程,面临列车动载大、软土侧压力大、土体渗流明显等基坑危害.通过优化设计,解决基坑支护安全性与经济性的矛盾.同时,在施工过程中,通过实时监测、变形预警和信息化施工等手段确保方案落实,减少动载振动影响和开挖时空效应.     

地铁设备监控系统的控制逻辑开发浅析

针对地铁建设过程中对监控系统的控制逻辑部分规划较少的问题,结合地铁工程实例,将控制逻辑与工程特点、系统特点和运营部门实际需求进行结合,对设备监控系统的控制逻辑部分进行介绍。     

北京地铁盾构隧道设计施工要点

对北京地铁5号线首次采用盾构法修建地铁隧道的情况进行了介绍,针对北京特有的地质条件,通过盾构试验段工程对设计和施工进行了系统的研究。     

信息化在北京地铁车辆检修中的创新与应用

北京地铁线网规模不断扩大,客流量不断增多,给地铁安全生产运营和车辆检修带来了越来越大的挑战。阐述应用信息化技术建立地铁安全生产管控信息平台辅助车辆检修管理的实践及探索,对平台架构、网络拓扑、功能模块、数据库的设计思路及实现过程进行详细叙述。对地铁车辆检修现状进行分析,产生需求;利用C/S与B/S模式相结合,研发出适用于地铁车辆检修的安全生产管控信息平台;应用智能手持终端及其APP的研发,实现车辆检修的标准化作业流程管控、数据采集,应用后台数据积累和程序运行,形成大数据分析。     

直线电机轨道交通轨道减振降噪技术

论述对直线电机轨道结构减振降噪的必要性,并就直线电机轨道交通振动与噪声的产生机理进行分析;在对加拿大及日本轨道结构的主要减振降噪措施进行分析的基础上,就我国在发展直线电机轨道交通时轨道结构减振降噪的主要研究思路进行初步探讨.     

基于滑模控制的卡尔曼滤波在列车定位中的研究

为了提高城市轨道交通中轮轴速度传感器与加速度计组合定位的精度,提出一种基于滑模控制的改进卡尔曼滤波算法。对于组合定位来说,由于卡尔曼滤波不能很好地修正加减速过程中的空转打滑误差,考虑到滑模控制器的滑动模态与系统的参数及扰动无关,提出采用基于滑模控制的改进卡尔曼滤波来进一步降低误差。其基本思路是应用指数趋近律滑模变结构来改善里程计算值,然后再进行卡尔曼滤波。并利用仿真软件对上述过程进行验证,仿真结果表明:基于滑模控制的改进卡尔曼滤波算法,能够在一定程度上减小空转打滑误差,进一步提高定位的精度。最后通过与其他卡尔曼滤波改进算法对比,得出基于滑模控制的卡尔曼滤波方法结构更为简单,也能保证一定的精度。     

Airline network choice and market coverage under high-speed rail competition

While the existing literature has focused on the short-term impacts, this paper investigates the long-term impacts of high-speed rail (HSR) competition on airlines. An analytical model is developed to study how an airline may change its network and market coverage when facing HSR competition on trunk routes. We show that prior to HSR competition, an airline is more likely to adopt a fully-connected network and cover fewer fringe markets if the trunk market is large. Under HSR competition, the airline will, for a given network structure, have a greater incentive to cover more fringe (regional or foreign) markets if the trunk market is large, or the airline network is close to hub-and-spoke. Further, the airline will, for any given market coverage, move towards a hub-and-spoke network when the trunk market is large, or the number of fringe markets covered by the airline network is large. Both effects are more prominent when the decreasing rate of airline density economies is large. We further show that HSR competition can induce the airline to adopt network structure and market coverage that are closer to the socially optimal ones, thereby suggesting a new source of welfare gain from HSR based on its long-term impacts on airlines. Implications for operators, policy makers and specific countries (such as China) are also discussed.     

Estimating the influence of common disruptions on urban rail transit networks

With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented.