非完全信息条件下交通分布预测时间(距离)法

对于新兴城镇和快速扩张城市的交通分布预测,由于信息缺乏很难采用传统的预测方法.本文提出了非完全信息下的交通分布预测时间(距离)法,并给出了其参数标定方法,对其可靠性也进行了实证性分析,最后,探讨了该模型对时间(或距离)的敏感性问题.     

质量动态管理在沥青混凝土路面施工中的应用

简要介绍了质量动态控制技术,并将平均值一极值控制图技术应用于广东茂湛二期高速公路的面层施工管理,实践表明,应用效果良好,路面施工质量得到良好地控制。展现了动态控制技术在沥青混凝土路面施工中的良好应用前景。     

高速公路交通流的分形维数与相空间重构预测

对成渝高速公路短时交通流通过计算不同时间尺度下Hurst指数而等到其相应的分形维数,结果表明,时间间隔越短的交通流,其分形维数越大,结构越复杂.由于时间间隔越短的交通流随机性大和复杂的结构,所以预测也就越困难.提出了一种新的基于相空间重构和移动平均相结合的预测方法——移动平均最近邻域法,从理论与实际数据两方面分析和验证了该方法对短时交通流预测的有效性.     

基于均值异质性随机参数Logit模型的城市道路事故驾驶员受伤严重程度研究

驾驶员事故严重程度诱因分析对减少伤亡事故具有重要意义,以往研究假定影响变量为固定参数容易导致参数估计及研究推论出现偏差,据此本文基于均值异质性的随机参数Logit模型深入研究城市道路事故驾驶员受伤严重程度.使用2015—2019年发生在贵阳市的道路交通事故数据,综合考虑驾驶员、车辆、道路、环境特征等潜在影响因素,同时利...     

船舶发生海损后的风险评估和控制

指出船舶发生海损后进行风险评估的必要性,根据危险评价的一般原则和方法对船舶发生海损后的处置措施提出评估方法和手段,有助于船东、海损事故处置决策人员正确选择处置措施。     

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.     

Impact of Sophisticated Stationary Forecast Techniques on the Bullwhip Effect in a Supply Chain

The bullwhip effect in a multistage supply chain was analyzed using sophisticated stationary forecasts (third order moving average and third order exponential smoothing forecasts). The third order exponential smoothing and third order moving average forecasts sometimes have a variance reducing effect on the supply chain.In a supply chain with positively correlated or independent and identically distributed (i.i.d) demands, the order variance based on simple moving average forecast (or simple exponential smoothing forecast) is larger than the order variance based on second order moving average forecast (or second order exponential smoothing forecast),and the order variance based on second order moving average forecast( or second order exponential smoothing forecast) is larger than the order variance based on third order moving average forecast( or third order exponential smoothing forecast). In addition, for i.i.d demands, third order exponential smoothing forecast leads to a larger variation than third order moving average forecast.     

平行钢丝拉索Daniel效应分析

为了研究平行钢丝拉索丹尼尔效应随钢丝数的变化规律,以85Φ5 mm钢丝拉索索力仿真计算为例,采用蒙特卡罗法分析了不同钢丝数目平行钢丝索的丹尼尔效应。应用Matlab软件生成钢丝抗拉强度随机样本,引入矩阵计算简化分析过程。通过回归分析得出拉索丹尼尔效应计算的计算式。研究结果表明,拉索钢丝抗拉强度的平均值、标准差及衰减因子随钢丝数增加而减小,其衰减规律服从多项式分布;当钢丝数在10~50之间时,拉索丹尼尔效应十分显著;当钢丝数在50~100之间时,丹尼尔效应逐渐减弱;当钢丝数大于100以后,随着拉索钢丝数的增加,钢丝的平均抗拉强度衰减缓慢,拉索丹尼尔效应趋于稳定。     

船舶操纵的FARMA模型模糊控制器研究

船舶操纵是一个非线性过程,传统控制方式的操纵性能不能令人满意。本文提出了一种不依靠任何系统模型的模糊控制器设计方法。仿真结果表明了它在船舶操纵应用中切实可行,在一定程度上改善了操纵性能。     

Urban traffic flow prediction: a spatio‐temporal variable selection‐based approach

Short‐term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio‐temporal‐based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high‐density road networks. Therefore, we propose a spatio‐temporal variable selection‐based support vector regression (VS‐SVR) model fed with the high‐dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two‐stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high‐dimensional spatio‐temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real‐world traffic volume collected from a sub‐area of Shanghai, China, demonstrate that the proposed spatio‐temporal VS‐SVR model outperforms the state‐of‐the‐art. Copyright © 2015 John Wiley & Sons, Ltd.