分局TMIS信息共享的开发

阐述了铁路分局TMIS进行二次开发实现信息共享的重要意义和主要内容,以及对TMIS二次开发中应注意的问题进行了分析,并提出了组织实施TMIS二次开发时的方法、建议.     

机车轴承故障诊断中的多智能传感器技术应用分析

故障诊断技术是保证铁路设备安全运行的一项重要技术.分析传统机车轴承故障诊断方法中存在的不确定性,利用多智能传感器技术进行机车轴承故障诊断,建立融合诊断系统.该系统采用决策级融合方法来处理由于单个参数带来的诊断的不确定性.实验表明,这种方法能够有效解决基于单个参数诊断系统中带来的不确定性问题.因而它具有很强的理论意义和应用价值.     

机务段综合信息管理平台的开发建设

铁路局直管站段体制确立后,机务段的规模、管辖范围进一步扩大,机车交路不断延长,乘务制式和劳动组织方式发生深刻的变化.因此,对各种信息的采集、传递、分析和处理在时间上和技术上提出更高要求.利用现代计算机信息技术,采用先进的B/S与C/S有机结合的模式开发建设适应新体制需要的机务段综合性信息管理平台.     

呼和浩特铁路局运输信息交换平台的建设

呼和浩特铁路局自1995年开始组织实施以TMIS(铁路运输管理信息系统)为核心的企业运输信息服务平台的建设,从根本上改变了传统的运输生产模式.介绍呼和浩特铁路局信息服务平台的基本框架,信息服务平台的应用集成等.     

基于P2P技术的信息获取方案研究

当前互联网的体系结构是客户/服务器模式,服务器集中了所有功能和资源,因此负荷很重,维护其上的信息检索越来越困难.对等网(P2P)技术可以解决这个问题.P2P网络是一种分布式系统结构,它将互联网的集中管理模式引向分散管理模式,将内容从中央单一节点引向网络的边缘,从而充分利用互联网中众多终端节点所蕴涵的处理能力和潜在资源.但是已有的P2P检索机制并不成功,分析指出,主要是缺少为web设计的、定义严密的、基本的数据模型,即信息定义和结构的质量很差.因此提出了一个基于P2P技术的信息获取方案-ICover,它建立在P2P网络上,提供信息建模、信息发布、信息网关、虚拟组织、信息端到端加密等关键部分,从而有效完成信息获取和共享.     

铁路局职称评审管理信息系统的设计与实现

职称评审管理信息系统实现对职称评审工作全过程的计算机管理,使职称评审工作进入信息化的新阶段.介绍系统的总体结构、系统功能、关键技术、实现方法和主要特点.     

基于Linux信息交换安全平台实现及应用模型研究

介绍信息交换平台数据库安全访问代理的需求及实现,对其安全性进行了分析.并在此基础上提出实际生产信息系统隔离与交互的应用模型,为今后的推广应用提供参考框架.     

The impact of real-time information on bus ridership in New York City

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.     

Communicating transportation carbon dioxide emissions information: Does gender impact behavioral response?

General concern and knowledge on climate change have been increasingly studied over the past decades. Gender differences have been found for general environmental concern and knowledge, but mixed findings exist with respect to climate change. In transportation, research has examined potential relations between environmental attitudes and transportation behavior, with mixed findings as well. Recently, the use of carbon dioxide (CO₂) emissions information to influence choice has been tested with women being found more willing to pay to reduce their personal impacts, suggesting that women are either more willing to change or that their response to information on climate change is stronger. However, those studies used CO₂ mass and studies that examined understanding of CO₂ information as a mass have found that people struggle to understand it. If concern and knowledge about climate change differ amongst individuals, then, according to theories such as the Transtheoretical Model, the type of information used to motivate choices is likely important. Using a unique data set (n = 236) it is possible to take a first look at how gender might affect concern, knowledge, and action in terms of transportation and climate change. Further, it is also possible to examine behavioral responses to transportation climate change information. Finally, an empirical analysis is conducted of the effect of how the information is presented might differ by gender. Thus, this work aims to investigate whether gender differences might contribute to the explanation of individual behavioral responses (from concern to action) in a transportation climate change context.     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.