Inferring patterns in the multi-week activity sequences of public transport users

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study, passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample (n = 33,026) from London’s public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users (n = 1973) is combined to smart card transactions to analyze associations between the identified patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.     

Overview of missing physical commodity trade data and its imputation using data augmentation

The physical aspects of commodity trade are becoming increasingly important on a global scale for transportation planning, demand management for transportation facilities and services, energy use, and environmental concerns. Such aspects (for example, weight and volume) of commodities are vital for logistics industry to allow for medium-to-long term planning at the strategic level and identify commodity flow trends. However, incomplete physical commodity trade databases impede proper analysis of trade flow between various countries. The missing physical values could be due to many reasons such as, (1) non-compliance of reporter countries with the prescribed regulations by World Customs Organization (WCO) (2) confidentiality issues, (3) delays in processing of data, or (4) erroneous reporting. The traditional missing data imputation methods, such as the substitution by mean, substitution by linear interpolation/extrapolation using adjacent points, the substitution by regression, and the substitution by stochastic regression, have been proposed in the context of estimating physical aspects of commodity trade data. However, a major demerit of these single imputation methods is their failure to incorporate uncertainty associated with missing data. The use of computationally complex stochastic methods to improve the accuracy of imputed data has recently become possible with the advancement of computer technology. Therefore, this study proposes a sophisticated data augmentation algorithm in order to impute missing physical commodity trade data. The key advantage of the proposed approach lies in the fact that instead of using a point estimate as the imputed value, it simulates a distribution of missing data through multiple imputations to reflect uncertainty and to maintain variability in the data. This approach also provides the flexibility to include fundamental distributional property of the variables, such as physical quantity, monetary value, price elasticity of demand, price variation, and product differentiation, and their correlations to generate reasonable average estimates of statistical inferences. An overview and limitations of most commonly used data imputation approaches is presented, followed by the theoretical basis and imputation procedure of the proposed approach. Lastly, a case study is presented to demonstrate the merits of the proposed approach in comparison to traditional imputation methods.     

铁路矿山法隧道BIM建模标准研究

针对铁路矿山法隧道BIM模型使用需求,研究施工图设计模型的构件命名规则与代码规则,以及建模精度与信息粒度等模型创建要求,建立铁路矿山法隧道施工图设计模型,建模精度、构件信息粒度包含施工工序和传统的二维施工图设计信息。研究成果符合现行的铁路隧道设计规范与标准要求,并结合依托工程得到成功实践。     

浅析地铁数字视频系统中的多级架构

对地铁安全防范系统中的视频监控子系统结构单一架构和多级架构进行比较;采用多级架构有利于构建稳定可靠的城市地铁监控系统,是整体联网联调的最佳解决方案;对数字视频系统多级系统架构关键技术进行详细描述.     

铁路信号动态检测数据管理系统

在铁路信号动态检测系统、列控设备动态监测系统和应答器数据管理系统运用的基础上,重点对信号检测项目、数据处理、设备管理等相关技术标准,数据转储设备,地面数据管理系统等进行研究。     

自组织特征映射网络在船舶雷达目标识别中的应用

针对船舶雷达目标识别存在的问题,研究了基于聚类的自组织特征映射网络(SOM网)的船舶识别。对SOM结构进行了设计,特别是在输出层设计、数据处理、数据聚类等方面进行重点的讨论。通过实验,SOM网络在船舶雷达目标识别中,具有较好的识别效果。     

贯彻《铁路数据通信网设计规范》积极促进铁路数据通信网发展

《铁路数据通信网设计规范》已经发布实施。概述了铁路数据通信网承载的业务,提出铁路数据通信网工程设计应遵循的共性要求,介绍了在网络结构、业务接入、路由策略、MPLSVPN部署、QoS、网络安全等方面的工程设计中,应如何理解、贯彻重点内容。     

船舶航行数据远程采集系统

介绍了一种基于卫星通信的船舶数据远程采集系统,该系统采用嵌入式控制设计,组合船舶C站和自动识别系统(AIS)设备组建系统,利用C站的卫星通信功能接收陆地远程控制指令,通过嵌入式控制系统实现对C站和AIS设备的远程控制。采集C站和AIS设备中的船舶航行信息,通过卫星通信将航行信息传送到陆地船舶管理系统,实现了船舶数据的远程采集,为提高船舶自动化和现代化管理水平提供了技术平台。     

基于B/S铁路财务数据审计预警系统设计与实现

介绍基于B/S方式的铁路财务数据审计预警系统的设计和实现过程,就设计思路、系统目标和功能模块进行阐述,并对关键技术的设计作了具体论述.     

车辆安全检测和管理系统的设计与实现

分析海量数据传输和管理系统的功能,并针对系统特点提出了基于C/S和B/S混合结构的解决方案,最后介绍了系统的设计与实现.