基于时间序列相似性搜索的交通流短时预测方法

为了进一步提高交通流短时预测的效果,在分析现有预测模型存在问题的基础上,设计了1种基于时间序列相似性搜索的交通流短时多步预测方法.利用界标模型对交通流时间序列数据进行模式表示,在历史数据库中搜索与当前交通流时间序列相似度较高的历史时间序列,进而确定与预测时刻相对应的历史数据,利用回声状态网络模型实现交通流的短时多步预测.采用某特大城市快速路5 min采样间隔的交通流量数据进行实验验证和对比分析.实验结果表明,回声状态网络模型的预测精度分别比ARIMA模型和BP神经网络模型提高了6.25%和3.85%,以时间序列相似性搜索结果作为模型输入数据能够进一步提高交通流短时预测的精度.      

人材机价格上涨对公路工程造价的影响

以某桥梁工程为例,通过定量分析人材机单价变化对桥梁工程造价带来的影响,建立预测模型。通过同样的分析手段,建立路基、路面、隧道的预测模型,作为指导今后工作的依据。     

Biocompatibility evaluation of polyethylene terephthalate artificial ligament coating hydroxyapatite by fibroblasts cells in vitro

Hydroxyapatite (HA) based materials have been widely used in the field of ligament tissue engineering in the past decades. It has been previously reported that HA can increase the penetration of marrow-derived mesenchymal stem cells (MSCs) and MSCs cells into scaffolds due to increased cell differentiation in biological media. Additionally, it was found that there are much difference between MSCs and anterior cruciate ligament (ACL) cells. For that reason, we mainly evaluate the biocompatibility of polyethylene terephthalate (PET) silk scaffold with fibroblasts cells in vitro. We cultured mouse fibroblasts cells on the substrate of PET fiber and PET-HA scaffold, respectively, and then observed the morphology by using scanning electron microscopy. Our data indicate that PET-HA scaffold has good biocompatibility with fibroblasts cells and can potentially be useful in enhancing the fibroblasts cell differentiation and proliferation.     

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轨道几何形状检测数据是一个随时间变化具有随机特征的时间序列,反映轨道几何状态的变化.在本文中,灰色关联度理论用于研究轨道水平不平顺时间序列数据,挖掘时间序列数据之间隐含的关系;经过普遍适应性改进和残差修正改进的灰色GM（1,1）模型预测固定测点轨道不平顺长期状态变化趋势,随机线性AR和卡尔曼滤波模型分析单元区段轨道不平顺短期变化趋势,探索轨道状态变化随机数据序列中隐藏的规律并进行预测.短期和长期预测模型验证结果表明,三种模型是有效的,能够达到预期的精度.     

考虑拉剪耦合作用地震边坡稳定性分析

基于拉剪耦合作用,对Mohr-Coulomb强度准则进行修正。在不同坡高、不同高宽比的边坡底部入射3条经典地震波,对边坡进行动力稳定性分析。首先采用显式迭代子增量法编写考虑拉剪耦合作用的Mohr-Coulomb强度准则子程序,并在边坡模型的截断边界添加黏弹性人工边界、输入等效地震荷载,模拟边坡受到的地震作用;然后以单元分别受到压缩和拉伸作用为例验证子程序的正确性;通过与已有地震边坡的研究结果对比,表明在地震边坡分析中若不考虑材料的拉剪耦合作用,得出的结论偏危险;最后在边坡底部垂直入射3条地震波,分析不同坡高、不同高宽比的边坡在2种强度准则下安全系数的区别。研究结果表明：不同边坡（坡高、高宽比不同）在不同地震波（El-Centro,Kobe,Northridge）作用下的安全系数各不相同,但安全系数都随坡高增加和坡角增大而降低;考虑拉剪耦合作用边坡的安全系数小于不考虑拉剪耦合作用边坡的安全系数,并且其安全系数的变化各有特点;坡高和高宽比的变化影响边坡对地震波频段的响应;分析地震边坡的稳定性,应综合考虑材料的拉剪耦合作用以及边坡高度和高宽比的变化规律。     

安徽省合裕线过闸运输船舶标准船型主尺度研究

结合安徽境内高等级航道航运特点,针对航域内主力船型开展标准船型主尺度系列研究。讨论船型主尺度系列制定的方法与原则,制定合裕线过闸运输船舶主尺度范围。研究成果已纳入京杭运河、淮河水系标准船型主尺度系列之中。     

连云新城海滩清淤工程维护效果研究

淤泥质海岸人工沙滩的维护问题日益为人们所关注。在平衡水深概念推导出的海床冲淤公式的基础上,通过对平衡流速的假设,增加了因水深改变而引起的海床冲淤,推广了适用于计算自然滩面下海床冲淤的公式。通过推广后的公式计算了连云新城海滩清淤工程的泥沙冲淤。计算结果表明：各清淤方案有使海床恢复到自然水深的趋势,且均有泥滩出露的现象,需要进行定期的疏浚维护。     

车辆作用下桥梁冲击系数分析

应用达朗贝尔原理推导了1/4车辆模型和桥梁的振动平衡方程;采用三角级数法生成各级桥面不平度序列;用数值方法分析了桥面平整度、车速及车辆参数对桥梁冲击系数的影响。结果表明:桥面不平度对桥梁冲击系数影响很大;冲击系数随着速度的增加而发生波动;合理的车辆参数可以减小对桥梁的冲击作用;车辆固有频率同样是影响冲击系数的一个不容忽视的因素。     

利用GNSS时序分析辨识大跨桥梁结构荷载响应研究

使用小波分析、功率谱密度等方法,对武汉市阳逻大桥GNSS监测数据进行分析,识别并分离大桥不同位置结构车辆移动荷载响应、车辆振动荷载响应和噪声,首先去除噪声,然后利用信号不同频率特性,分离静态信号和动态信号,从而得到更为清晰的大跨度桥动力响应特性。     

Modelling heavy vehicle car‐following behaviour in congested traffic conditions

This study develops a car‐following model in which heavy vehicle behaviour is predicted separately from passenger car. Heavy vehicles have different characteristics and manoeuvrability compared with passenger cars. These differences could create problems in freeway operations and safety under congested traffic conditions (level of service E and F) particularly when there is high proportion of heavy vehicles. With increasing numbers of heavy vehicles in the traffic stream, model estimates of the traffic flow could be degrades because existing car‐following models do not differentiate between these vehicles and passenger cars. This study highlighted some of the differences in car‐following behaviour of heavy vehicle and passenger drivers and developed a model considering heavy vehicles. In this model, the local linear model tree approach was used to incorporate human perceptual imperfections into a car‐following model. Three different real world data sets from a stretch of freeway in USA were used in this study. Two of them were used for the training and testing of the model, and one of them was used for evaluation purpose. The performance of the model was compared with a number of existing car‐following models. The results showed that the model, which considers the heavy vehicle type, could predict car‐following behaviour of drivers better than the existing models. Copyright © 2013 John Wiley & Sons, Ltd.