Dynamic and disequilibrium analysis of interdependent infrastructure systems

There is a growing awareness in recent years that the interdependencies among the civil infrastructure systems have significant economic, security and engineering implications that may influence their resiliency, efficiency and effectiveness. To capture the various types of infrastructure interdependencies and incorporate them into decision-making processes in various application domains, Zhang and Peeta (2011) propose a generalized modeling framework that combines a multilayer infrastructure network (MIN) concept and a market-based economic approach using computable general equilibrium (CGE) theory and its spatial extension (SCGE) to formulate a static equilibrium infrastructure interdependencies problem. This paper extends the framework to address the dynamic and disequilibrium aspects of the infrastructure interdependencies problems. It briefly reviews the static model, and proposes an alternative formulation for it using the variational inequality (VI) technique. Based on this equivalent VI formulation, a within-period equilibrium-tending dynamic model is proposed to illustrate how these systems evolve towards an equilibrium state within a short duration after a perturbation. To address a longer time scale, a multi-period dynamic model is proposed. This model explicitly considers the evolution of infrastructure interdependencies over time and the temporal interactions among the various systems through dynamic parameters that link the different time periods. Using this model, numerical experiments are conducted for a special case with a single region to analyze the sensitivity of the model to the various parameters, and demonstrate the ability of the modeling framework to formulate and solve practical problems such as cascading failures, disaster recovery, and budget allocation in a dynamic setting.     

Generalized transport costs and index numbers: A geographical analysis of economic and infrastructure fundamentals

We rely on the economic theory approach to index numbers to improve the existing definitions and decompositions of variations in generalized transport costs (GTCs). As a value index, we decompose GTCs into price and quantity indices associated to economic—market—costs and infrastructure variables—distance and time within a network. The methodology allows the accurate identification of the sources of GTCs decline. We illustrate it for the case of road freight transportation in Spain between 1980 and 2007 and at a highly detailed geographical level. Average GTCs weighted by trade flows have decreased by 16.3%, with infrastructure driving that reduction. We find large territorial disparities in GTCs, but also significant geographical clusters where the market and network indices show spatial association.     

Bicycle commuting in Melbourne during the 2000s energy crisis: A semiparametric analysis of intraday volumes

Cycling is attracting renewed attention as a mode of transport in western urban environments, yet the determinants of usage are poorly understood. In this paper we investigate some of these using intraday bicycle volumes collected via induction loops located at ten bike paths in the city of Melbourne, Australia, between December 2005 and June 2008. The data are hourly counts at each location, with temporal and spatial disaggregation allowing for the impact of meteorology to be measured accurately for the first time. Moreover, during this period petrol prices varied dramatically and the data also provide a unique opportunity to assess the cross-price elasticity of demand for cycling. Over-dispersed Poisson regression models are used to model volumes at each location and at each hour of the day. Seasonality and the impact of weather conditions are modelled as semiparametric and estimated using recently developed multivariate penalized spline methodology. Unlike previous studies that use aggregate data, the empirical results show a substantial meteorological and seasonal component to usage. They also suggest there was substitution into cycling as a mode of transport in response to increases in petrol prices, particularly during peak commuting periods and by commuters originating in wealthy and inner city neighbourhoods. Last, we extend the approach to a multivariate longitudinal count data model using a Gaussian copula estimated by Bayesian data augmentation. We find first order serial dependence in the hourly volumes and a ‘return trip’ effect in daily bicycle commutes.     

Promoting carsharing attractiveness and efficiency: An exploratory analysis

Carsharing has grown significantly over recent years. Understanding factors related to the usage and turnover rate of shared cars will help promote the growth of carsharing programs. This study sets station-based shared car booking requests and turnover rates as learning objectives, by which generalized additive mixed models are employed to examine various effects. The results are: (1) stations with more parking spaces, longer business hours and fewer nearby stations are likely to receive more booking requests and have a higher turnover rate; (2) an area with a higher population density, a higher percentage of adults, a higher percentage of males, a greater road density, or more mixed land use is associated with more car usage and a higher turnover rate; (3) stations nearby transit hubs, colleges, and shopping centers attract more shared car users; (4) shared cars are often oversupplied at transit hubs; (5) both transit proximity and housing price present high degrees of nonlinearity in relation to shared car usage and turnover rates. Findings provide evidence for optimizing the usage and efficiency of carsharing programs: carsharing companies should identify underserved areas to initiate new businesses; carsharing seems more competitive in a distance to a bus stop between 1.2 km and 2.4 km, and carsharing is more effectively served in areas with constraints in accessing metro services; carsharing should be optimally discouraged at transit hubs to avoid the oversupply of shared cars; local authorities should develop a location-based and geographically differentiated quota in managing carsharing programs.     

A phase-based smoothing method for accurate traffic speed estimation with floating car data

In this paper, a novel freeway traffic speed estimation method based on probe data is presented. In contrast to other traffic speed estimators, it only requires velocity data from probes and does not depend on any additional data inputs such as density or flow information. In the first step the method determines the three traffic phases free flow, synchronized flow, and Wide Moving Jam (WMJ) described by Kerner et al. in space and time. Subsequently, reported data is processed with respect to the prevailing traffic phase in order to estimate traffic velocities. This two-step approach allows incorporating empirical features of phase fronts into the estimation procedure. For instance, downstream fronts of WMJs always propagate upstream with approximately constant velocity, and downstream fronts of synchronized flow phases usually stick to bottlenecks. The second step assures the validity of measured velocities is limited to the extent of its assigned phase. Effectively, velocity information in space-time can be estimated more distinctively and the result is therefore more accurate even if the input data density is low.The accuracy of the proposed Phase-Based Smoothing Method (PSM) is evaluated using real floating car data collected during two traffic congestions on the German freeway A99 and compared to the performance of the Generalized Adaptive Smoothing Method (GASM) as well as a naive algorithm. The quantitative and qualitative results show that the PSM reconstructs the congestion pattern more accurately than the other two. A subsequent analysis of the computational efficiency and sensitivity demonstrates its practical suitability.     

ROV的研发现状及发展趋势

  目的  针对船体、大坝、水下钢结构等表面附着物的安全检测,以及附着物清除作业的要求,需要研制一款新型水下安全检测和作业型带缆遥控水下机器人（ROV）。此类ROV需针对不同作业任务更换机械手,实现抓取、切割的功能,从而保证去除结构物上的附着物。  方法  阐述水下安全检测和作业型机器人的整套系统组成及原理。该系统以Arduino单片机为控制面板信号采集工具、以水面监控系统开发工控机为平台,零浮力脐带缆以2对双绞线、1对电源线为信号电力传输线,水下控制系统以ARM嵌入式为主控单元。建立ROV动力学模型,设计ROV艏向控制的广义预测控制器。经过系统调试,进行水池试验和湖上试验。  结果  试验证明,该系统运行正常,整套控制系统的稳定性、可靠性、实时性均达到设计要求,满足水下安全检测及作业要求。  结论  该系统设计方案和控制算法对于其他水下机器人的控制系统研究均有借鉴意义。     

利用矩阵的迹与行列式估计特征值的界

设A是具有正实数特征值λ1≥λ2≥…λn＞0的一个方阵,令1≤k≤l≤n,我们得到λ1＋λ2＋…λk和λk＋λk＋1＋…＋λ1的上界,并将得到的界与原有的上界做了简单比较.     

国际集装箱多式联运与东部港口竞争力分析

从北方中西部地区外贸货物国际集装箱多式联运的路径优化分析入手，分别通过对经上海、青岛和连云港转运的全程广义运输成本的估算，以及概率型交通量分配模型的应用，就我国东部港口之间的竞争力水平进行了实证研究。     

基于支持向量机的船舶航向广义预测控制研究

船舶的动态性能具有大惯性、大时滞、非线性等特点,采用基于结构风险最小化原则的神经网络———支持向量机(SVM),充分发挥其可以任意逼近非线性模型的良好特性对船舶模型进行有效辨识,以此作为广义预测控制(GPC)算法中的预测模型,并加以相应的GPC算法达到保持航向的目的。仿真结果表明:SVM学习速度快,在小样本情况下具有良好的非线性建模和泛化能力;基于SVM的GPC算法在航向保持方面具有很好的控制性能。     

m×n广义行列式按一行展开的性质

笔者曾在“m×n广义行列式的定义及运算性质”一文中详细介绍了m×n广义行列式的概念及运算性质。为了今后研究问题方便，本文将给出它的另外一个重要的运算性质——按一行展开定理。这个性质在某种程度上将简化广义行列式的运算，但更主要的是在理论上的应用价值。