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.     

基于K均值聚类与Bayesian分类器的车型识别方法

不同车型经过感应线圈时,线圈检测器采集过来信号变化的波形不同,同类车型对应采集的波形中如波峰极大值、峰值位置、平均值、波峰数等特征值都具有相似性。根据感应线圈采集所得信号的特点,讨论了一种基于K均值聚类与Bayesian分类器的车型识别方法。学习训练中,通过K均值聚类算法把不同波形的特征值归类,再采用Bayesian分类器算法去获得分类器。实验结果表明,该方法具有较高的车型识别精度。     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

汽车企业开发二三级市场应解决5大问题

广阔的二三级市场成为中国汽车消费增长的新起点,也成为各大汽车厂商的必争之地。但是,这类市场数量众多,需求量大,汽车企业开辟二三级市场面临着严峻考验。目前,很多企业已经意识到二三级市场的重要性,但对于如何进入这个市场大都束手无策。文章指出,当前汽车企业开发二三级市场应解决5大问题：汽车企业开发二三级市场重视程度不足;口碑指数下降;销售人员业务水平不高;业务不全、价格偏高以及渠道建设创新。     

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。     

一种目标综合识别能力评估方法

目标综合识别能力是C^3I系统重要指标之一。运用统计估计的基本原理和方法，分析用测试样本集估计识别正确率的可行性以及相应的估计性能，定义目标综合识别能力指标，并给出目标综合识别能力的实验室测试评估方法。可为C^3I系统目标综合识别能力测试提供参考。     

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。