LINEAR SYSTEMS AND LINEAR INTERPOLATION I

Suppose V is a vector space over the real orcomplex field F.A linear transformation T on Vis a function T from V into itself such that T(αx βy) =αTx βTy holds for any vectors x,y∈ V,and any scalarsα,β∈ F.Linear transformationsare applied to represent linear systems.A systemis said to interpolate[1] a family Soflinear transfor-mations if for each input of the system the corre-sponding output can be obtained by the action ofsome member of S.Thatis,if foreach x∈V,Tx= Qxx,for some …     

高寒地区建筑物饰面砖大面积脱落的原因及防治

高寒地区砖混结构的建筑物外墙饰面砖常因温度变化而产生脱落。在对脱落的原因进行分析的基础上 ,提出了在设计和施工中应采取的措施。     

新疆国际旅游客源量的线性组合预测

利用新疆历年来的统计数据,探讨线性组合预侧方法在新疆境外客流量预侧中的应用 ,为新疆维吾尔自治区政府制定旅游发展规划提供科学依据.     

基于RBM-GASA-BPNN 的潜在高价值旅客预测

针对用BP神经网络(Back Propagation Neural Network, BPNN)进行潜在高价值旅客预测时出现的特征表达能力弱、稳定性差、易陷入局部极值的不足,提出一种新颖的基于 RBM-GASA-BPNN的潜在高价值旅客预测方法.该方法首先通过聚类算法划分旅客类别,设置类别标签;然后利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)提取旅客行为特征并确定最优BPNN 初始权值和偏置的寻优范围,又利用遗传模拟退火算法(Genetic Algorithm-Simulate Anneal, GASA)对BPNN参数进行精调,确定了最优的BPNN初始权值和偏置;最后,利用优化后的BPNN对旅客进行分类预测.实验结果表明,本文提出的方法克服了基于BPNN的分类预测方法的缺陷,具有更高的分类预测准确率和潜在高价值旅客预测能力.     

基于组合深度学习的快速路车道级速度预测研究

随着物联网、云计算和大数据在智能交通领域的普及应用,传统的以道路断面为研究对象的预测方法已经无法满足智能网联技术发展的需求.本文以车道断面为研究对象,提出一种基于组合深度学习(Combined Deep Learning,CDL)的城市快速路车道级速度预测模型.该模型利用基于信息熵的灰色关联分析提取空间特征变量,采用长短期记忆神经网络提取空间特征变量的时间特征,并利用门限递归单元神经网络得到预测结果.通过北京市东二环路车道断面实测微波数据验证发现,提取车道交通流的时空特征,CDL模型能够很好地拟合不同车道不同时段的速度变化趋势,可有效地实现车道速度的单步及多步预测,且该模型的预测精度和稳定性均优于传统预测模型.     

新疆铁路发展因素及指标分析

铁路对祖国西部边睡的新疆具有重要的意义,铁路的发展影响着新疆经济的发展.通 过对影响铁路发展的业内及业外因素的因子分析,找出影响其发展的重要因子;同时利用新疆 铁路运输历年来发展主要指标的统计数据,探讨线性组合预测方法在新疆铁路运输主要发展指 标预侧中的应用为国家、新疆维吾尔自治区政府及新疆铁路运输企业制定新疆铁路发展规划 提供科学依据.     

车站班计划和阶段计划计算机编制方法的研究

针对现行车站班计划和阶段计划编制方法存在编制时间长、计划不准确、车流推算落后等弊端,提出在车站计算机信息系统中增加"班计划编制模块"和"阶段计划编制模块",并运用计算机辅助推算车流,以实现车站自动化指挥生产,从而使运输工作有序可控,达到提高运输效率和运输效益的目的.     

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.     

Hurricane evacuation demand models with a focus on use for prediction in future events

Although substantial literature exists on understanding hurricane evacuation behavior, few studies have developed models that can be used for predicting evacuation rates in future events. For this paper, we develop new ordered probit models for evacuation using survey data collected in the hurricane-prone state of North Carolina in 2011 and 2012. Since all covariates in the models are available from the census or based on location, the new models can be applied to predict evacuation rates for any future hurricane. The out-of-sample predictive power of the new models are evaluated at the individual household level using cross validation, and the aggregated level using available data from Hurricane Irene (2011), Hurricane Isabel (2003) and Hurricane Floyd (1999). Model results are also compared with an existing participation rate model, and a logistic regression model available from the literature. Results at the individual household level suggests approximately 70% of households’ evacuation behavior will be predicted correctly. Errors are evenly divided between false positives and false negatives, and with accuracy increasing to 100% as the percentage of people who actually evacuate goes to zero or all and decreasing to about 50% when the population is divided and about half of all households actually evacuate. Aggregate results suggest the new models compare favorably to the available ones, with average aggregate evacuation rate errors of five percentage points.     

A robust optimization approach for dynamic traffic signal control with emission considerations

We consider an analytical signal control problem on a signalized network whose traffic flow dynamic is described by the Lighthill–Whitham–Richards (LWR) model (Lighthill and Whitham, 1955; Richards, 1956). This problem explicitly addresses traffic-derived emissions as constraints or objectives. We seek to tackle this problem using a mixed integer mathematical programming approach. Such class of problems, which we call LWR-Emission (LWR-E), has been analyzed before to certain extent. Since mixed integer programs are practically efficient to solve in many cases (Bertsimas et al., 2011b), the mere fact of having integer variables is not the most significant challenge to solving LWR-E problems; rather, it is the presence of the potentially nonlinear and nonconvex emission-related constraints/objectives that render the program computationally expensive.To address this computational challenge, we proposed a novel reformulation of the LWR-E problem as a mixed integer linear program (MILP). This approach relies on the existence of a statistically valid macroscopic relationship between the aggregate emission rate and the vehicle occupancy on the same link. This relationship is approximated with certain functional forms and the associated uncertainties are handled explicitly using robust optimization (RO) techniques. The RO allows emissions-related constraints and/or objectives to be reformulated as linear forms under mild conditions. To further reduce the computational cost, we employ a link-based LWR model to describe traffic dynamics with the benefit of fewer (integer) variables and less potential traffic holding. The proposed MILP explicitly captures vehicle spillback, avoids traffic holding, and simultaneously minimizes travel delay and addresses emission-related concerns.