一种新的支持向量回归算法及其在集装箱吞吐量预测中的应用

支持向量机是基于统计学习理论框架下的一种新的通用机器学习方法,是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,该技术已成为当前国际机器学习界的研究热点,能较好地对应解决小样本、高维数、非线性和局部极小点等实际问题。近来,SVR方法被引入求解回归和预测问题,并在各领域中得到广泛的应用。文章提出了一种新的基于单参数的Lagrangian支持向量回归算法,并将该算法应用在集装箱吞吐量预测中。估算结果证明了这种改进的支持向量回归算法在集装箱吞吐量预测中的有效性和实用性。     

Estimating salinity to complement observed temperature: 2.Northwestern Atlantic

This paper addresses the problem of estimating salinity for a large region in the Atlantic Ocean containing the Gulf Stream and its recirculation. Together with Part 1 [Thacker, W.C., 2007-this issue. Estimating salinity to complement observed temperature: 1. Gulf of Mexico. Journal of Marine Systems. doi:10.1016/j.jmarsys.2005.06.008.] dealing with the Gulf of Mexico, this reports on the first efforts of a project for developing world-wide capability for estimating salinity to complement expendable-bathythermograph (XBT) data. Such estimates are particularly important for this region, where the strong frontal contrasts render the task of assimilating XBT data into numerical models more sensitive to the treatment of salinity.Differences in salinity's co-variability with temperature and with longitude, latitude, and day-of-year from the northwestern part of the region with the Gulf Stream to the southeastern part more characteristic of the Sargasso sea suggested that the region be partitioned to achieve more accurate salinity estimates. In general, accuracies were better in the southeastern sub-region than in the more highly variable northwestern sub-region with root-mean-square estimation errors of 0.15 psu at 25 dbar and 0.02 psu at 300 dbar as compared with 0.35 psu and 0.50 psu, respectively, but in the southeast there was an unexpected error maximum around 1000 dbar where estimates were slightly less accurate than in the northwest. For pressures greater than 1400 dbar root-mean-square errors in both sub-regions were less than 0.02 psu.     

Part 1. National road safety performance: Data,the emergence of two single-outcome modeling streams and public health

This first part of the state-of-the art focuses on the origins of road safety modeling, covering data, early models and the public health context of model formulation and use.     

隧道变形预测的灰色与回归模型对比分析

模型预测法是目前常用的隧道围岩变形预测的方法之一。文章结合广梧高速公路茶林顶隧道工程实例,建立GM（1,1）灰色模型、GM（2,1）灰色模型和双曲函数回归模型分别对隧道围岩变形进行预测,并对各模型的预测情况进行对比分析。结果表明,不论是从短期还是从长期看,GM（1,1）灰色模型都体现了优越的模拟和预测效果,且建立预测模型时不需要大量的统计数据,可应用于工程实际。     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

物流企业客户满意度评价中回归分析方法的应用

在激烈的竞争中,物流企业服务的质量及客户的满意度高低是至关重要的因素.文章以真实的企业数据为依据,提出了一种基于多元回归分析的多指标评价方法,并从客观性、实用性、推广性出发,介绍了该方法的具体应用,对物流企业提高客户满意度、改进服务具有较强的指导性与普及性作用.     

基于误差反传神经网络的船型要素建模分析方法及应用

探讨用ＢＰ神经网络建立船型要素建模分析理论和方法。讨论了建模的处理技巧。结合算例对基于ＢＰ网络的数学模型的表达方式、网络结构模型预报精度的影响及模型中变量的重要度分析方法等基本问题作了初步分析。     

Short-Term Traffic Flow Forecasting for Freeway Incident-Induced Delay Estimation

Freeway incidents not only threaten travelers’ safety but also cause severe congestion. Incident-induced delay (IID) refers to the extra travel delay resulting from incidents on top of the recurrent congestion. Quantifying IID would help people better understand the real cost of incidents, maximize the benefit-cost ratio of investment on incident remedy actions, and develop active traffic management and integrated corridor management strategies. By combining a modified queuing diagram and short-term traffic flow forecasting techniques, this study proposes an approach to estimate the temporal IID for a roadway section, given that the incidents occurs between two traffic flow detectors. The approach separates IID from the total travel delay, estimates IID for each individual incident, and only takes volume as input for IID quantification, avoiding using speed data that are widely involved in previous algorithms yet are less available or prone to poor data quality. Therefore, this approach can be easily deployed to broader ranges where only volume data are available. To verify its estimation accuracy, this study captures two incident videos and extracts ground-truth IID data, which is rarely done by previous studies. The verification shows that the IID estimation errors of the proposed approach are within 6% for both cases. The approach has been implemented in a Web-based system, which enables quick, convenient, and reliable freeway IID estimation in the Puget Sound region in the state of Washington.     

试验构建鼓式制动器的摩擦模型

针对鼓式制动器在高制动初速、高气压下制动力矩的台架试验值与设计值存在较大偏差的问题，对试验测量的制动器制动力矩进行二元线性回归分析，建立了鼓式制动器的幂函数摩擦模型。进行了显著性检验和失拟性检验，检验结果验证了所建立的摩擦模型有效，可提高整车制动效能模拟计算精度。     

水泥红粘土的强度试验研究

红粘土是碳酸盐岩系出露区的岩石,在我国分布广泛,不同地区的红粘土表现的工程性质具有差异性。就湘中南地区的粘性红壤土通过不同水泥掺入比及不同龄期下的无侧限抗压强度试验,分析了水泥红粘土的强度增长机理,对试验结果进行了回归分析,给出了不同掺量、不同龄期水泥土之间的强度推算公式。