广州地铁行车关键岗位岗前考试信息系统研发

为了提高城市轨道交通行车组织关键岗位业务技能和安全意识,广州地铁在司机、调度指挥人员、车站值班员等行车关键岗位推行岗前考试制度.通过研发岗前考试系统,对业务流程和关键数据流程进行分析,提出了系统数据库访问方法和关键查询存储过程,实现关键岗位岗前考试常态化、标准化、信息化,确保地铁行车组织安全有序.     

铁路行包管理信息系统的远程监控系统设计与开发

针对铁路行包管理信息系统的远程监控问题,分析监控需求,研究并设计远程监控系统的结构,重点探讨监控信息的采集、传输、输出3项关键技术的实现方式,并实现各种监控功能.     

接触网检修管理信息系统的设计

介绍接触网检修管理系统,系统包括巡视结果输入、检修计划的安排、检修计划执行情况的统计和设备质量评定等模块.系统采用C/S(Client/Server)结构,软件采用Delphi技术,具有跨平台,便于维护和升级,安全性好等优点.     

车站综合管理信息系统的应用与安全运行

信息系统在车站已得到广泛的应用,保证其功能完善、流程优化、安全可靠、加快推进是当前车站综合管理信息系统建设的重要课题.从郑州铁路局车站综合管理信息系统建设的实践出发,提出具体解决方案.     

智能网络视频监控系统在铁路工程建设管理中的应用

着重阐述视频监控技术的发展现状.详细介绍智能网络视频监控系统的典型组成结构和功能特点,并对其在铁路工程建设管理信息化中的应用和重要作用进行论述.     

基于模糊神经网络的航海信息融合技术研究

根据航海信息融合的要求，建立了航海信息融合处理模型，讨论了各种传感器数据的融合处理；采用改进的自适应模糊系统的径向基函数网络的信息融合算法实现目标信息融合，建立数学模型，给出了具体的算法，并通过计算机仿真，验证了该信息融合算法对提高目标数据的精度和可靠性的有效性。     

图形编辑系统中从面状目标到其属性的查询

地理信息系统的图形编辑系统中，从面状目标到其属性查询的关键在于经过多边形内点判别快速地检索出该目标的图形数据与属性数据相互关联的计算机内码。通过对传统面状目标的数据结构改进和对构成面状目标周边的坐标串预处理，利用Ｂｏｒｌａｎｄ Ｃ＋＋环境进行程序设计与调试，在试验系统中实现了从而状目标到其属性的查询，大大地降低了多边形内点判别的运算量，明显的提高了查询的效率。     

智能交通系统综合信息平台研究

分析了综合交通信息平台数据处理、交互平台和辅助决策等主要功能，在此基础上提出了以多种数据处理技术为核心的综合交通信息平台的分布式体系结构，探讨了综合交通信息平台建设的数据融合技术、数据压缩技术、数据标准、数据仓库技术以及数据挖掘技术等，该综合信息平台的具有实现交通运输系统的有效集成、交通运输服务内容的延伸等优越性。     

Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

Traffic prediction is critical for the success of intelligent transportation systems (ITS). However, most spatio-temporal models suffer from high mathematical complexity and low tune-up flexibility. This article presents a novel spatio-temporal random effects (STRE) model that has a reduced computational complexity due to mathematical dimension reduction, with additional tune-up flexibility provided by a basis function capable of taking traffic patterns into account. Bellevue, WA, was selected as the model test site due to its widespread deployment of loop detectors. Data collected during the 2 weeks of July 2007 from 105 detectors in the downtown area were used in the modeling process and traffic volumes predicted for 14 detectors for the entire month of July 2008. The results show that the STRE model not only effectively predicts traffic volume but also outperforms three well-established volume prediction models, the enhanced versions of autoregressive moving average (ARMA) and spatiotemporal ARMA, and artificial neural network. Even without further model tuning, all the experimental links produced mean absolute percentage errors between 8% and 16% except for three atypical locations. Based on lessons learned, recommendations are provided for future applications and tune-up of the proposed STRE model.     

Solving the K-shortest paths problem in timetable-based public transportation systems

Finding the K-shortest paths in timetable-based public transportation systems is an important problem in practice. It has three typical variants: the K-earliest arrival problem (K-EAP), the K-shortest travel time problem (K-STTP), and the K-minimum number of transfers problem (K-MNTP). In this article we show that these problems can be solved efficiently by first modeling the timetable information with the time-expanded approach, then applying the Martins and Santos (MS) algorithm. Then we model the timetable information with the time-dependent approach and propose a modified version of the MS algorithm for solving the K-EAP. Experimental results on real-world data show that for K smaller than 100, which is enough for most applications, the execution times of the MS algorithm for the problems in the time-expanded model are less than 100 ms on a server with a 1.86-GHz central processing unit (CPU) and 4 GB of memory. For solving the K-EAP the modified MS algorithm in the time-dependent model is even more efficient (about three times faster for K ≤ 100) than the original algorithm in the time-expanded model. Our results imply the great potential of the MS algorithms in practical transportation service systems.