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。     

隧道病害维修保养技术方案设计

为保障铁路隧道安全运营,在分析国内外车辆技术、作业平台技术和检测技术现状的基础上,从车辆技术、作业平台技术、检测技术和作业模式4方面,提出隧道病害维修保养技术方案设计思路,设计一套整合隧道病害维修保养相关技术装备及检测模式方案。该方案采用内燃电传动及蓄电池电传动集成的混合动力传动系统,搭载三作业平台,配备探地雷达系统、手持雷达、音频检测系统和高速成像系统,采用智能巡检、机械复检和人工专检的检测作业模式,是适应我国铁路发展的隧道病害维修保养可行方案。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

运营期高速铁路轨道长波不平顺静态测量方法及控制标准

现有高速铁路轨道长波不平顺静态检测主要采用矢距差法或简化矢距差法,存在与检测起点相关、含有里程相位差、基础变形时检测幅值偏大、与车体振动加速度匹配性较差等缺点。利用中点弦测法对轨道长波不平顺进行静态检测,通过对中点弦测法不同测弦长度有效测量波长范围和列车敏感波长分析,采用60 m测弦长度的中点弦测法最适合时速300~350 km运营期高速铁路;利用车辆-轨道动力学仿真分析和最小二乘法拟合相结合方法,提出运营期高速铁路300及350 km·h^-1速度下的轨道长波高低不平顺控制标准,并进行实例验证。结果表明:60 m弦中点弦测法既可保证轨道长波不平顺检测的准确性,又能很好地体现车体振动响应;时速300 km运营期高速铁路轨道长波高低不平顺3级控制标准建议值分别为9,15,21 mm;时速350 km分别为7,11,15 mm。     

舱口盖强度计算方法

在某集装箱舱口盖结构强度的有限元计算中，对舱口盖周围水平限位器和支撑块处的约束以及顶部压紧器处的关联程度进行了模拟，分别讨论了在垂向设计载荷的情况下，水平限位器、支撑块处的不同约束以及顶部压紧器的不同关联程度对舱口盖应力分布的影响，得出更符合实际情况的舱口盖结构强度计算方法。     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

载人潜器人因工程研究

对载人潜器人因工程的研究现状、研究范畴、技术手段和发展趋势进行梳理和总结,旨在为指导载人潜器设计总体集成优化、提高潜器综合效能、节省全寿命周期费用及缩减潜器人员编制提供参考。     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

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.