外贸出口对广东经济增长的实证分析

外贸经济之于广东省的经济发展无疑是有重要作用的,本文将在充分借鉴已有研究成果的经验基础上,基于年度时间序列数据,进行必要的处理,适当选取变量,运用较为普遍的计量经济学方法建立模型,对广东外贸出口促进经济增长的内在规律和贡献程度进行实证分析,并根据结果,对广东如何通过外贸出口促进经济增长提出一些政策性的建议。     

我国综合交通系统的社会贡献评价研究——以2000-2017年数据为例

本文首先介绍了研究背景,提出研究目的。通过梳理国内外相关文献,提出现有研究尚未涉及综合交通系统社会贡献方面。在解析综合交通社会贡献机理基础上,根据我国2000—2017年社会系统和综合交通数据确定我国社会系统发展指数S和交通系统发展指数T的组成指标,在每种交通方式内采用因子分析法确定各指标权重,结合各指标数值得到S和T数值。应用格兰杰检验法证明T是S的原因,由S关于T的回归函数可得我国综合交通社会贡献评价函数,处理后得到最佳状态数值,发现实际数值与之相比仍有较大提升空间。最后,提出相关政策建议。     

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.     

Modeling the cost of bird strikes to US civil aircraft

The objective of our analysis is to develop a model of damage costs that arise from collisions between aircraft and birds, based on data drawn from the Federal Aviation Administration National Wildlife Strike Database (NWSD). We develop a two-part model, composed of two separate statistical models, that accounts for the effects of aircraft mass category, engine type, component of the aircraft struck, and the size and number of birds struck. Our results indicate the size of bird, number of birds, and engine ingestions are the largest determinants of strike-related costs. More generally, our result is a model that provides a better understanding of the determinants of damage costs and that can be used to interpolate the substantial amount of missing data on damage costs that currently exists within the NWSD. A more complete accounting of damage costs will allow a better understanding of how damage costs vary geographically and temporally and, thus, enable more efficient allocation of management resources across airports and seasons.     

Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring

We present a statistical process control framework to support structural health monitoring of transportation infrastructure. We contribute an integrated, generally-applicable (to various types of structural response data) statistical approach that links the literatures on statistical performance modeling and on structural health monitoring. The framework consists of two parts: The first, estimation of statistical models to explain, predict, and control for common-cause variation in the data, i.e., changes, including serial dependence, that can be attributed to usual operating conditions. The ensuing standardized innovation series are analyzed in the second part of the framework, which consists of using Shewhart and Memory Control Charts to detect special-cause or unusual events.We apply the framework to analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from April 1, 2010 to June 29, 2011. Our analysis reveals that, after controlling for seasonal effects, linear trends are significant components of the response measurements. Persistent displacement may be an indication of deterioration of the bridge supports. Trends in the strain data may indicate changes in the material properties, i.e., fatigue, sensor calibration, or traffic loading. The results also show that autocorrelation and conditional heteroscedasticity are significant sources of common-cause variation. Use of the control charts detected 43 possible special-cause events, with approximately 50% displaying persisting effects, and 25% lasting longer than one week. Analysis of traffic data shows that unusually heavy loading is a possible cause of the longest special-cause event, which lasted 11 days.     

边坡极限平衡分析中结构面抗剪强度的确定

研究目的:为了在边坡极限平衡分析中,能够正确确定控制性结构面的抗剪强度,从而为边坡的稳定性分析及其防护工程提供可靠的依据。研究方法:在系统研究Barton公式的基础上,结合2个边坡工程现场勘察取样、室内实验、提出了结构面粗糙度的确定方法,并通过实例验证。研究结果:提出了采用坡度均方根系数来确定JCS的方法,拟合出二者的关系式,提高了其准确性和适用性;对于软弱结构面,提出根据充填厚度(平直型软弱结构面)或充填度(粗糙型软弱结构面)确定其抗剪强度参数的方法和原则。研究结论:研究表明,在粗糙度系数准确测量的基础上,Barton估算方法提供的岩体结构面抗剪强度参数,可直接用于工程岩体稳定性评价,该方法预测岩体结构面抗剪强度是可靠的,且具有简便、经济、快速等优点,特别适用于公路、铁路路堑边坡稳定性计算评价。     

基于单参数的Lagrangian支持向量回归算法及其应用

支持向量机(Support Vector Machines,SVM)是基于统计学习理论框架下的一种处理非线性分类和非线性回归的有效方法。由于具有完备的理论基础和出色的学习性能,该方法已成为当前国际机器学习界的研究热点,能较好地解决小样本、高维数、非线性和局部极小点等实际问题。这里提出了一种基于单参数的拉格朗日(Lagrangian)支持向量回归算法,并将该算法应用在外贸货物吞吐量预测中。估算结果证明了这种改进的支持向量回归算法在吞吐量预测中的有效性和实用性。     

Estimating salinity to complement observed temperature: 1. Gulf of Mexico

This paper and its companion [Thacker, W.C., Sindlinger, L., 2007-this issue. Estimating salinity to complement observed temperature: 2. Northwestern Atlantic. Journal of Marine Systems. doi:10.1016/j.jmarsys.2005.06.007.] document initial efforts in a project with the goal of developing capability for estimating salinity on a region-by-region basis for the world oceans. The primary motivation for this project is to provide information for correcting salinity, and thus density, when assimilating expendable-bathythermograph (XBT) data into numerical simulations of oceanic circulation, while a secondary motivation is to provide information for calibrating salinity from autonomous profiling floats. Empirical relationships between salinity and temperature, which can be identified from archived conductivity–temperature–depth (CTD) data, provide the basis for the salinity estimates.The Gulf of Mexico was chosen as the first region to explore for several reasons: (1) It's geographical separation from the Caribbean Sea and the North Atlantic Ocean makes it a “small ocean” characterised by a deep central basin surrounded by a substantial continental shelf. (2) The archives contain a relatively large number of CTD data that can be used to establish empirical relationships. (3) The sharp fronts associated with the Loop Current and its rings, which separate water with different thermal and haline characteristics, pose a challenge for estimating salinity. In spite of the shelf and the fronts, the relationship between salinity and temperature was found to be sufficiently regular that a single empirical model could be used to estimate salinity on each pressure surface for the entire Gulf for all seasons. In and below the thermocline, root-mean-square estimation errors are small — less than 0.02 psu for pressures greater than 400 dbar, corresponding to potential density errors of less than 0.015 kg/m³. Errors for estimates nearer to the surface can be an order of magnitude larger.     

小波去噪在船舶操纵运动建模中的应用

通过向20°/20°Z形试验仿真数据中添加随机噪声,得到含噪声的试验数据;随后,应用最小二乘支持向量回归机(Least Square-Support Vector Regression,LS-SVR)对经过小波去噪的试验数据和含噪声的试验数据进行分析,辨识了船舶操纵运动二阶线性响应模型中的操纵性指数。将由去噪试验数据和含噪试验数据得到的20°/20°Z形试验预报结果同20°/20°Z形试验仿真数据进行对比,验证了小波去噪在对含噪声的Z形试验数据进行去噪处理的有效性。