Prospects for computer aided railway scheduling: perspectives from users and parallels from mass transit

Railway scheduling faces new challenges as competition and, in many parts of the world, privatisation require ever better results, both in terms of minimising the resources used and maximising the performance achieved by those resources. It is therefore surprising that in practice railway scheduling tasks appear to be performed largely without the use of optimizing models. This paper takes two different perspectives, a case study of user needs from the UK railway industry and a comparative analysis with mass transit scheduling systems, to consider why this might be and assesses what the future prospects are for computer aided railway scheduling.

A number of conclusions are drawn: that optimization models need to be integrated with software packages that meet schedulers' data management needs; that researchers need to work with commercial developers to achieve this integration; that there needs to be an emphasis on extending models to cover more real‐life circumstances; and that solutions with international applicability should be sought.     

A spatial Difference-in-Differences estimator to evaluate the effect of change in public mass transit systems on house prices

Evaluating the impact of public mass transit systems on real-estate values is an important application of the hedonic price model (HPM). Recently, a mathematical transformation of this approach has been proposed to account for the potential omission of latent spatial variables that may overestimate the impact of accessibility to mass transit systems on values. The development of a Difference-in-Differences (DID) estimator, based on the repeat-sales approach, is a move in the right direction. However, such an estimator neglects the possibility that specification of the price equation may follow a spatial autoregressive process with respect to the dependent variable. The objective of this paper is to propose a spatial Difference-in-Differences (SDID) estimator accounting for possible spatial spillover effects. Particular emphasis is placed on the development of a suitable weights matrix accounting for spatial links between observations. Finally, an empirical application of the SDID estimator based on the development of a new commuter rail transit system for the suburban agglomeration of Montréal (Canada) is presented and compared to the usual DID estimator.     

TLMD阻尼器在桥梁工程上的应用研究

为有效抑制九江长江大桥三大拱吊杆的涡激振动,曾为其加装了TMD阻尼器.近20 a来其涡激振动得到了控制,但却造成了阻尼器长期大幅振动状态下的疲劳损伤.为避免当损伤积累后将削弱减振效果并危及结构安全,依托该桥三大拱吊杆新型液体-质量双调谐阻尼器(TLMD)研发项目,研究了阻尼器安装质量比与主振系统及阻尼系统动力放大系数的关系,并对阻尼器振动幅值进行了理论分析.研究发现:为保证阻尼器的安全与可靠,应选取合理的阻尼器安装质量比,使得该类阻尼器的动力放大系数不超过30倍.并据此指导了该桥吊杆新增阻尼器实施方案的设计.测试结果表明减振效果良好,且未见阻尼器疲劳损伤现象.     

大体积混凝土裂缝控制综述

简述大体积混凝土裂缝产生的原因,裂缝预测,现场控制,材料选择,混凝土的养护。     

城市轨道交通桥梁纵向制动力传递分析

以城市轨道交通桥梁为对象,分析制动力作用于轨道面上的传递过程。将轨道、扣件、桥跨结构、支座、墩身及基础划成有限元,建立平衡方程,分析当列车制动时,纵向力的传递与分配。由于无缝线路的联系作用,制动力将分配至较广范围,每一桥墩承受的最大制动荷载只相当于有缝线路制动力的39 6%～56 8%。制动过程中,纵向制动力对钢轨引起的应力不大。当采用扭矩为60N·m的扣件,墩身高度超过10m时,制动过程中局部区段的扣件可能出现滑动现象。     

Numerical prediction of the dilution process and its biological impacts in CO2 ocean sequestration

 In order to investigate the biological impacts of the ocean sequestration of CO₂ (carbon dioxide), the dilution processes of CO₂ were investigated near injection points in the deep ocean. From a combined fluid-dynamics, chemical, and biological approach, a two-phase computational fluid dynamics (CFD) method with mass transfer was developed to predict droplet plume flow, the dissolution of CO₂ from droplets into seawater, and the advection–diffusion of dissolved CO₂ (DCO₂) in the deep ocean. Changes in pH due to the concentration of DCO₂ were also calculated. In addition, the isomortality concept of Auerbach et al. was incorporated to predict the lethal damage to marine organisms caused by DCO₂. The simulation results suggested that the biological impacts of CO₂ sequestration were insignificant in terms of mortality in both small-scale field experiments and the real-life cases we propose. Received: October 3, 2001 / Accepted: December 14, 2001     

大规模定制的汽车底盘产品族建模方法研究

在建立汽车底盘产品的客户需求层次分析模型的基础上,论述应用层次分析法确定客户需求权重的方法和过程,以客户需求为依据拟定底盘产品系列型谱。在构建底盘功能体系结构的同时,将产品的功能域与产品物理域映射方法及产品子功能相关性计算、判断方法应用于底盘产品。确定底盘产品的模块划分方案并建立底盘产品族结构模型。     

数字图书馆现状与发展

数字图书馆是-个研究如何驱动、组织多媒体海量数字信息以及其在互联网上的应用等各方面问题的新技术领域,它以电子格式存储海量的多媒体信息,并能对这些信息资源进行高效的操作.     

Water masses and cyclonic circulation in Bay of La Paz, Gulf of California, during June 1998

The Bay of La Paz, a coastal and shallow basin of the Gulf of California (depth<420 m), exchanges mixed-layer waters with the Gulf of California predominantly through Boca Grande (Big Mouth), in the northeastern part of the bay. Equatorial Surface Water (ESW) flows from the gulf to the bay; once there, due to evaporation processes, this water increases its salinity above 35.00, therefore becoming Gulf of California Water (GCW) Mass. During June 1998, the baroclinic circulation in the bay was dominated by a cyclonic gyre. The hydrographic dome, related to this gyre, is depicted until approximately 150-m depth. However, the vertical mixing of water between the mixed-layer and the ones below practically does not occur because of the development of a sharp pycnocline. This fact and the isolation of the bottom of the bay by a bathymetric sill (approximately 250-m depth) at Boca Grande induce low oxygen content, particularly in the bottom-waters (O₂<0.1 ml/l). Chlorophyll values are higher and with larger vertical fluctuations within the Bay of La Paz than outside.     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.