关于铁路综合IP数据网建设方案的设想

介绍了中国铁路数据通信网现状，以及建设铁路综合数据网的必要性。提出了建设铁路综合数据网的方案，并从网络功能结构、规划原则以及工程实施等方面进行论述。     

智能分类器方法

小样本数据不能对分类器进行充分的训练。传统的分类器方法一旦设计好,再也不会有任何改进.本文将人工智能的思想、方法应用于分类器设计中,提出了智能分类器概念。智能分类器不但可以对未知样本进行分类识别,还具有多专家决策、预分类、学习等功能。智能分类器是一种自适应系统,其系统参数可在识别的过程中得到不断的优化。在ORL人脸库上的实验结果证明该方法在解决小样本问题时具有明显的优势。     

铁路货票信息的数据挖掘

从三个方面对货票数据进行挖掘。即:运用集合理论构造关系数据库特征关联模型,描述了特征规则知识的表达:rule(E,T,Cp),给出rule(E,T,Cp)的发现算法和算法的应用举例,实例表明铁路多年来把标重作为计算整车费率唯一因素的办法应作适当调整;运用聚类方法对货票数据进行聚类,描述了聚类的步骤,对某公司2000年度的运距和收入进行聚类,分析聚类结果对铁路生产经营、市场营销的启示;运用ARIMA模型对货票数据进行季节性知识挖掘,描述挖掘的过程,用1992年～2001年的历史数据预报2002年货运量。     

数据生产管理特点分析

以数据信息生产企业为例 ,对数据生产管理特点进行分析 ,为此类企业建设有效的企业管理系统提出相应的建议。     

汽车结构用钢的进展

概述了汽车结构钢的研究进展，列举了与提高汽车、发动机可靠性相关的合金结构钢、调质钢、非调质钢、渗碳钢等钢种的发展；较详细论述了与汽车的轻量化、节能、降低排放、提高安全性等密切相关的高强度钢和先进高强度钢的研究概况、典型应用以及今后的发展趋势。     

铁路局车站间数据交换系统的设计与实现

在铁路运输生产中,车站起着重要的作用.讨论铁路局调度系统中铁路局与车站数据交换的实现方法,利用Socket技术使得数据交换变得准确和快捷.实践表明,该系统能够很好地满足日常运输生产要求.     

基于BP神经网络的货车车号识别方法研究

针对货车车号的字体不规则、单字有断裂的特点,提出基于图像预处理的动态字符分割和提取算法,准确地对车号图像进行车号区域提取和车号单字分割.利用Hilditch细化算法对单字进行细化,提取出能表征数字的结构特征向量.设计出BP人工神经网络,数字的结构特征向量作为BP网络的输入,用经过训练的BP网络进行货车车号的识别.     

Evaluating the added-value of online bus arrival prediction schemes

Online predictions of bus arrival times have the potential to reduce the uncertainty associated with bus operations. By better anticipating future conditions, online predictions can reduce perceived and actual passenger travel times as well as facilitate more proactive decision making by service providers. Even though considerable research efforts were devoted to the development of computationally expensive bus arrival prediction schemes, real-world real-time information (RTI) systems are typically based on very simple prediction rules. This paper narrows down the gap between the state-of-the-art and the state-of-the-practice in generating RTI for public transport systems by evaluating the added-value of schemes that integrate instantaneous data and dwell time predictions. The evaluation considers static information and a commonly deployed scheme as a benchmark. The RTI generation algorithms were applied and analyzed for a trunk bus network in Stockholm, Sweden. The schemes are assessed and compared based on their accuracy, reliability, robustness and potential waiting time savings. The impact of RTI on passengers waiting times are compared with those attained by service frequency and regularity improvements. A method which incorporates information on downstream travel conditions outperforms the commonly deployed scheme, leading to a 25% reduction in the mean absolute error. Furthermore, the incorporation of instantaneous travel times improves the prediction accuracy and reliability, and contributes to more robust predictions. The potential waiting time gains associated with the prediction scheme are equivalent to the gains expected when introducing a 60% increase in service frequency, and are not attainable by service regularity improvements.     

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.     

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.