Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

解析铁道国家标准《铁路道岔转辙机》

铁路道岔转辙机的安全性和可靠性直接影响铁路行车安全,为满足现有使用条件,修订GB/T 25338(所有部分)—2010《铁路道岔转辙机》,形成GB/T 25338(所有部分)—2019《铁路道岔转辙机》。介绍标准修订背景,标准主要修订内容和修订依据,详细说明耐振性能相关规定,并提出标准实施建议。     

多分类转导支持向量机

为了完成分类学习,传统的支持向量机基于带标记信息的经验数据归纳出一个通用的决策函数。而转导支持向量机则不同,它考虑包含测试集在内的所有数据信息并致力于最小化测试样本的分类错误数。在已有的2类分类方法的基础上构造了直接求解多类分类问题的的转导支持向量机。     

浅谈机械化清扫在我省高速公路上的应用

通过论述机械化清扫在高速公路上应用的基本情况、操作规程、注意事项等内容，旨在为机械化养护在高速公路上的进一步开展提供可借鉴的经验。     

我国铁路养路机械技术标准体系研究

概述我国铁路养路机械发展历程,简要介绍国际国外养路机械相关标准情况,对比分析我国铁路养路机械标准。提出基于我国高速、普速、重载三大技术标准体系研究成果的铁路养路机械标准体系框架,以及包括现行有效的铁道国家标准、铁道行业标准、中国铁路总公司标准、标准性技术文件、正在制修订的标准以及标准制定建议组成的标准明细表建议。     

ZDJ9-K型转辙机表示电路分析与故障判断

ZDJ9-K型转辙机运用于客运专线和高速铁路,通过对其表示电路原理分析,结合郑西高铁运用实际情况,阐述各种表示电路故障的判断方法,为现场工作人员判断处理故障提供参考借鉴。     

无刷直流电机在电动转辙机中的应用

分析了ZD6型电动转辙机有刷电机的缺点,介绍了无刷直流电机取代有刷电机的方案及可靠性措施。     

大型立式车床噪声分析

本文详细地介绍了作者对EQF—382大型立式奉床噪声的分析过程,阐明了机床低噪声优化设计中进行噪声测试分析的重要性。测试分析结果可为该类机床结构设计提供参考依据。     

一种新型曲轴磨床数控系统

介绍一种PC总线内含PLC功能的CNC数控系统,以及对H175数控曲轴磨床实现改造的方法。