Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Scheduling heterogeneous train traffic on double tracks with efficient dispatching rules

To further improve the utilization rate of railway tracks and reduce train delays, this paper focuses on developing a high-efficiency train routing and timetabling approach for double-track railway corridors in condition that trains are allowable to travel on reverse direction tracks. We first design an improved switchable policy which is rooted in the approaches by Mu and Dessouky (2013), with the analysis of possible delays caused by different path choices. Then, three novel integrated train routing and timetabling approaches are proposed on the basis of a discrete event model and different dispatching rules, including no switchable policy (No-SP), Mu and Dessouky (2013)’s switchable policy (Original-SP) and improved switchable policy (Improved-SP). To demonstrate the performance of the proposed approaches, the heterogeneous trains on Beijing–Shanghai high speed railway are scheduled by aforementioned approaches. The case studies indicate that in comparison to No-SP and Original-SP approaches, respectively, the Improved-SP approach can reduce the total delay of trains up to 44.44% and 73.53% within a short computational time. Moreover, all of the performance criteria of the Improved-SP approach are usually better than those of other two approaches.     

基于改进的PESP高速铁路列车运行图研究

借鉴国内外铁路列车运行图的编制理论和方法,基于周期规划事件(Period Event Scheduling Problem,PESP)的思想,构建周期性列车运行图模型。考虑我国列车运行组织的特点及周期性列车运行图模型的求解复杂性,对模型进行改造,以列车的总停站时间和总运行时间最小为目标,建立到发顺序为已知条件的周期运行图模型。改造后的模型为整数线性规划模型,大大降低模型求解复杂度。借助通用建模软件GAMS/COINBONMIN,编程求解得到列车的周期运行时刻表。     

事件记录仪在地铁列车故障分析中的应用

介绍了国产地铁列车事件记录仪的性能参数和工作原理,通过2次事例,具体分析事件记录仪在实际应用中的故障判别方法,并在使用过程中对事件记录仪进行优化改进,使其发挥更大的作用。     

考虑交通事件影响的城市道路行程时间预测

外部环境因素对城市交通预测有较大影响，尤其在交通事件发生时，由于交通流的随机性和非线性特征，交通异常情况下的预测精度往往较低。为此，基于深度学习理论，提出一种以序列到序列模型（Sequence-to-sequence，Seq2Seq）为主体，融合外部因素特征的城市道路行程时间预测方法。利用时间序列分解算法（Seasonal and Trend Decomposition Using Loess，STL）挖掘交通历史数据的时序周期规律，结合交通事件数据深入分析交通异常产生的原因，并建立堆叠降噪自编码器模型（Stacked Denoising Autoencoder，SDAE）提取时间属性和交通事件数据的潜在特征。以北京市北四环中路和G6京藏高速路段为例，对预测模型的准确性和可行性进行验证，通过重复性交通事件和非重复性交通事件下的案例试验，对SDAE组件的有效性进行分析。研究结果表明：模型的单步和多步预测性能均优于基线模型，预测精度最高达到了87.71%；与其他输入了交通事件数据的模型相比，以SDAE作为外部组件的模型具有较好的预测性能和鲁棒性，能够适应复杂多变的交通流，在智能交通系统的短期预测上有显著的优越性，可以增强管理系统的调控能力，降低城市交通的拥堵成本。     

新闻(事件)营销的现状与对策

阐述了新闻(事件)营销的含义及特征,分析了当前事件营销运作现状,并提出了提高新闻(事件)营销效果的对策.     

从查询式到事件驱动的软件接口开发技术

通过对一款电话语音卡软件接口的分析，利用windows操作系统中对多线程程序设计提供的相关支持，提供了在查询式软件接口上开发基于事件驱动模型的软件接口的方法。采用提供的方法可以使用户的应用软件的设计变得较为简单。     

基于单片机的数字钟设计及时间校准研究

用单片机来设计数字钟，软件实现各种功能比较方便,但因软件的执行需要一定的时间，所以就会出现误差，对比实际的时钟，查找出误差的来源，并作出调整误差的方法，使得误差尽可能地小，使得系统可以达到实际数字钟的允许误差范围内。     

异步事件驱动的物联网服务模型

随着物联网应用的迅速发展,网络服务面对高并发请求,物联网服务和拥有大量在线用户的交通管理与信息网络服务器随时面临巨大访问量冲击,对网络服务质量形成威胁。本文提出了一种高性能网络服务模型。该模型基于EPCIS,用“阶段”划分网络服务,对象化的队列为主要构件,底层采用异步I／O技术,组件内部和组件间的通信采用事件驱动模型构建。设计和实现了一个基于事件驱动模型的异步Socket通信适配子层,用以屏蔽底层0S异步I／O细节。实验证明该模型对高并发请求服务具有较好的可伸缩性,对网络服务的业务流程变化具有较好的的敏捷性和适应性。