材料性能数据管理平台开发及应用

材料性能参数是设计研发阶段产品设计仿真的基础,也是产品最终使用性能的保障。基于此,文章通过对国内外材料数据管理系统及发展趋势研究,基于目前流行的B/S架构,采用MVC分层设计模式,进行了材料数据发布、分析、应用和废止的全生命周期管理平台系统的开发,能够支持材料数据管理、对比分析,并能输出分析软件直接使用的材料卡片,实现同工程分析的无缝对接。材料性能数据管理平台在材料数据管理和应用层面具有很好的实用价值。     

成像雷达在港口交管系统应用的探讨

成像雷达广泛应用到各个领域.通过对成像雷达在港口交通管理系统中的可行性,以及整个系统的功能、组成的应用分析.探讨了成像雷达的优势,系统的设计与实现.     

基于环形线圈检测器采集信息的交通状态分类方法应用研究

在智能交通系统中,交通状态判别算法通常被用来进行道路环境中实时交通状态的判断。这些算法将外场设备采集到的实时交通流数据与既有的交通状态分类标准特征作比较,来识别交通系统运行的状态。应用聚类分析方法,结合数据预备技术和交通工程技术,对环形线圈监测系统采集的交通流基础特征数据进行挖掘,实现了一种交通状态分类方法,并对交通管理控制系统中实时交通状态的判断识别提供可靠的参照标准。     

基于非负矩阵分解的高速列车走行部工况识别

根据高速列车振动信号数据的特点,针对高速列车走行部工况的识别,提出了一种基于非负矩阵分解(NMF)算法的工况识别模型。该模型通过数据处理获得具有辨识度的工况数据集,使用K-meams、谱聚类、近邻传播和模糊C均值4种聚类算法获得初步识别结果。聚类算法的初步识别结果作为NMF算法的输入,获得最终识别结果。相比NMF算法与聚类算法的识别结果,NMF算法具有更高的准确率,本文验证了NMF算法在高速列车工况识别方面的有效性和高效性,为高速列车的工况识别提出了一种有效的解决方案。     

铁路通信网质量人工智能评估系统的研究及应用

梳理和总结了铁路通信网的维护管理要求,结合铁路通信网的“全寿命管理”需求和未来的“智能运维”理念,针对适合于铁路特点的数据分析、评估决策场景进行算法研究和模型建立,采用自动采集、多维存储、大数据分析和“管理驾驶舱”可视化等新技术,研究建设了铁路通信网质量人工智能评估系统,实现对骨干通信网质量数据的全寿命期的采集、分析及管理,为网络优化调整、业务资源变更和修程修制改革提供数据基础,为推广全路通信网的质量管理提供了科学依据。     

Lightweight Occupancy Estimation on Freeways Using Extended Floating Car Data

Advanced traffic management systems rely heavily on technology to perform accurate estimations of the current state of the traffic as well as its short-term evolution. The objectives are improving traffic flow and enhancing road safety. Their success is based on accurate monitoring of two key variables, specifically speed and occupancy. The latter of the two has, to date, received significantly less attention from the scientific community. In this work we present a lightweight method to perform “on-line” occupancy estimation. We first propose three occupancy measurements calculated from data collected by a floating car: vehicle count, percentage of stop time, and headway. We then extend these discrete values to a continuous estimation of occupancy in space and time. The proposed estimators are based on a pairwise linear regression of each of the previously calculated measurements over certain references obtained from other floating cars or magnetic loop detectors. The method has been calibrated and validated under real traffic conditions and data. Despite the ease of implementation, the method is able to reproduce the occupancy values generated by the actual loop detectors, achieving promising results, with estimation errors down to 6.52%, even before multivehicle systems are considered.     

辽宁省交通工程质量检测与数据智能分析平台技术应用

辽宁省目前在建高速公路监理实验室应用并推广《交通工程质量检测与数据智能分析平台》,介绍了该系统了总体技术方案及推广应用具有的重大意义。     

Using DEA models to jointly estimate service quality perception and profitability – Evidence from international airports

With increasing competition between airports and a growing share of non-aeronautical revenues, particularly at large international hubs, perceived quality and consumer satisfaction have not only become a key focus of management but potentially the most important tool to achieve or maintain a competitive advantage. However, in the long run stakeholders aim for profit maximisation and it has been shown for other parts of the aviation supply chain that quality does significantly impact on profitability. In this paper we aim to investigate whether perceived airport quality has an impact on airport profit margins. We further apply two-stage Data Envelopment Analysis (DEA) models to estimate a single efficiency measure that combines the potentially conflicting indicators of perceived service quality and profitability for the airport context. We also identify determinants of the jointly estimated single quality/profitability measure.     

Prediction of arrival times of freight traffic on US railroads using support vector regression

Variability of travel times on the United States freight rail network is high due to large network demands relative to infrastructure capacity, especially when traffic is heterogeneous. Variable runtimes pose significant operational challenges if the nature of runtime variability is not predictable. To address this issue, this article proposes a data-driven approach to predict estimated times of arrival (ETAs) of individual freight trains, based on the properties of the train, the properties of the network, and the properties of potentially conflicting traffic on the network. The ETA prediction problem from an origin to a destination is posed as a machine learning regression problem and solved using support vector regression trained and cross validated on over two years of detailed historical data for a 140 mile section of track located primarily in Tennessee, USA. The article presents the data used in this problem and details on feature engineering and construction for predictions made across the full route. It also highlights findings on the dominant sources of runtime variability and the most predictive factors for ETA. Improvement results for ETA exceed 21% over a baseline prediction method at some locations and average 14% across the study area.     

Capturing the conditions that introduce systematic variation in bike-sharing travel behavior using data mining techniques

The potential of smart-card transactions within bike-sharing systems (BSS) is still to be explored. This research proposes an original offline data mining procedure that takes advantage of the quality of these data to analyze the bike usage casuistry within a sharing scheme. A difference is made between usage and travel behavior: the usage is described by the actual trip-chaining gathered with every smart-card transaction and is directly influenced by the limitations of the BSS as a public renting service, while the travel behavior relates to the spatio-temporal distribution, the travel time and trip purpose. The proposed approach is based on the hypothesis that there are systematic usage types which can be described through a set of conditions that permit to classify the rentals and reduce the heterogeneity in travel patterns. Hence, the proposed algorithm is a powerful tool to characterize the actual demand for bike-sharing systems. Furthermore, the results show that its potential goes well beyond that since service deficiencies rapidly arise and their impacts can be measured in terms of demand. Consequently, this research contributes to the state of knowledge on cycling behavior within public systems and it is also a key instrument beneficial to both decision makers and operators assisting the demand analysis, the service redesign and its optimization.