Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

深水碎石整平系统整平试验研究

对深水碎石整平系统进行介绍,分析了整平系统的核心装备——抛石整平头的各项性能,并给出了对应的定性与定量的分析。介绍了整平系统位于半潜驳上的刮平试验情况,对试验数据进行了分析。此试验采用西门子PLC,集合以太网通讯技术,采集GPS高程参数,采用位置算法,集成了防超差功能,对抛石头油缸进行了自动同步精度控制。试验结果表明抛石系统满足设计要求,该系统的分析可为此类工程系统的设计与制作提供参考。     

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.     

走廊层面交通小区划分方法的优化

基于公交客运走廊划分的交通小区，可为走廊公交线网优化提供更为符合公交乘客出行特征的需求分析和预测基础.本研究针对公交客运走廊特点，将走廊划分为直接影响区和间接影响区，提出分层次的交通小区划分理论.针对走廊不同影响区在需求预测和分析中对结果精度要求的不同，基于大数据选取更加适合公交走廊交通小区划分的聚类指标，提出直接影响区细分和间接影响区合并的划分方法，并通过引入聚类因子初步确定聚类数目和交通小区中心对传统聚类方法进行改进，克服了传统聚类方法随机选取聚类数目和中心而影响聚类精度的不足.最后，基于多源异构大数据对广渠路走廊进行实例验证，结果表明，本研究提出的分层次小区划分方法较传统方法在适用于公交客运走廊需求分析和预测划分精度方面更优.     

无接触网有轨电车车地无线传输系统研究

针对无接触网有轨电车设计了一套基于大数据分析技术及物联网技术的车地无线传输系统。介绍了该系统的系统架构、系统功能及设计要点。该系统能够显著提升列车车地数据传输的安全性、高效性及便捷性,提高了无接触网有轨电车车地传输技术和智能化应用水平。同时,该系统具有良好的可扩展性,可满足各类城市轨道交通车辆车载设备调试、维护以及数据传输的应用需求。     

浅谈鲁南高铁CSD瞬时干扰率优化工作

在鲁南高铁曲阜东至日照段联调联试中.日照西站附近总是出现CSD瞬时干扰问题.为此对网络、设备进行研究,分析A和Abis接口监测数据码流,查看有关参数设置是否匹配.找出问题所在,修改设备参数后解决了问题。     

Exploring the role of social capital influence variables on travel behaviour

This paper explores the potential role of individual trip characteristics and social capital network variables in the choice of transport mode. A sample of around 100 individuals living or working in one suburb of Madrid (i.e. Las Rosas district of Madrid) participated in a smartphone short panel survey, entering travel data for an entire working week. A Mixed Logit model was estimated with this data to analyze shifts to metro as a consequence of the opening of two new stations in the area. Apart from classical explanatory variables, such as travel time and cost, gender, license and car ownership, the model incorporated two “social capital network” variables: participation in voluntary activities and receiving help for various tasks (i.e. child care, housekeeping, etc.). Both variables improved the capacity of the model to explain transport mode shifts. Further, our results confirm that the shift towards metro was higher in the case of people “helped” and lower for those participating in some voluntary activities.     

城市轨道交通运营施工安全控制方式

地铁运营线路施工的计划管理、作业组织受区域、时间、作业条件等多种因素影响,易出现安全风险.安全控制作为消除风险的基础手段,贯穿施工管理业务全程.鉴于此,以天津地铁实际运用中的施工管理方式为基础,探讨了施工管理流程,并对天津地铁施工安全控制基本方式进行了分析,对信息化条件下的安全控制手段进行了说明.     

A cell-based logit-opportunity taxi customer-search model

This paper proposes a cell-based model to predict local customer-search movements of vacant taxi drivers, which incorporates the modeling principles of the logit-based search model and the intervening opportunity model. The local customer-search movements were extracted from the global positioning system data of 460 Hong Kong urban taxis and inputted into a cell-based taxi operating network to calibrate the model and validate the modeling concepts. The model results reveal that the taxi drivers’ local search decisions are significantly affected by the (cumulative) probability of successfully picking up a customer along the search route, and that the drivers do not search their customers under the random walk principle. The proposed model helps predict the effects of the implementation of the policies in adjusting the taxi fleet size and the changes in passenger demand on the customer-search distance and time of taxi drivers.