An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

On the income elasticity of the value of travel time

Transport infrastructure is long-term and in appraisal it is necessary to value travel time savings for future years. This requires knowing how the value of time (VTT) will develop over time as incomes grow. This paper investigates if the cross-sectional income elasticity of the VTT is equal to inter-temporal income elasticity. The study is based on two identical stated choice experiments conducted with a 13 year interval. Results indicate that the relationship between income and the VTT in the cross-section has remained unchanged over time. As a consequence, the inter-temporal income elasticity of the VTT can be predicted based on cross-sectional income elasticity. However, the income elasticity of the VTT is not a constant but increases with income. For this reason, the average income elasticity of the VTT in the cross-sections has increased between the two survey years and can be expected to increase further over time.     

适于TBM施工的HSP法实时预报技术设计与实现

为实现TBM施工隧道的实时地质预报,以TBM刀盘滚刀破岩震动作为震源的HSP法地质预报技术为基础,通过分析TBM施工工艺与机身结构特点,对HSP系统硬件和软件进行优化设计,使其小型化、自动化和智能化,并搭载于TBM上进行智能控制。在TBM掘进过程中,首先,通过隧道轮廓位置的检波器连续或高频次地采集地震反射波信号; 然后,经系统软件对数据的自动处理,实时获取掌子面前方地层反射特征参数图谱; 其次,通过智能识别技术完成对不良地质反射界面的有效拾取与判识; 最后,实现不良地质的探测。通过对适于TBM施工的HSP法实时预报技术进行优化设计,实时探查前方地层不良地质(体)位置、规模与性质,为TBM的高效施工起到一定的推助作用。     

动态载荷识别的自适应延迟逆模型方法

为减小载荷识别问题对原系统先验知识的依赖,采用系统的自适应延迟逆模型识别时域载荷。采用自适应算法辨识延迟逆模型,代替了一般识别方法中的系统特性矩阵求逆过程,避免了病态问题。随后将工作状态下的响应作为逆模型的输入,则其输出就是时域载荷的延迟估计。通过对两端简支梁结构进行载荷识别的仿真研究,以及对双层隔振试验台架的试验研究,识别了稳态激励和瞬态激励,验证了该方法的有效性。该方法不需要了解系统的数学模型及参数,因此能够应用于工程实践中。     

基于AQWA的双浮箱-双水平板浮式防波堤水动力分析

运用AQWA软件,针对双浮箱-双水平板浮式防波堤,建立不规则波作用下浮体锚链的耦合模型,研究时域情况下,改变水平板与浮箱的垂直间距,分析浮体运动响应以及锚链受力情况;并深入探讨了不同波浪周期和波高情况下水平板长度对浮体运动响应以及锚链受力的影响.结果表明,增加水平板与浮箱的垂直间距和水平板长度,可以有效减小浮体运动响应和锚链受力.波高和波浪周期是浮体重要的水动力因素,波高越大,波浪周期越长,浮体运动响应越剧烈;当浮体水平板长度达到21米左右时,整个防波堤达到最佳锚泊状态.     

SIR-20地质雷达在工程中的应用及图谱解释

介绍了地质雷达探测的基本原理,并结合工程实例和模拟试验说明了地质雷达的具体应用。通过对地质雷达检测图谱的分析和归类处理,找出目标体的地质雷达图谱特征,为地质雷达在工程中的应用提供了判别依据。     

基于混沌时间序列的道路断面短时交通流预测模型

为了提高道路断面短时交通流预测的精确性,本文对道路断面的短时交通流数据进行混沌时间序列分析,并对多维交通流时间序列数据进行了相空间重构,建立基于混沌时间序列分析的道路断面短时交通流预测模型,利用粒子群优化算法优化模型的参数选择．最后应用本文的方法对城市快速路采集的断面交通流数据进行分析,对道路断面短时交通流建立预测模型并验证其有效性．     

水泥—水玻璃浆液的室内试验与研究

分析了水泥浆液中掺入水玻璃对水泥凝结时间和无侧限抗压强度的影响。试验结果表明,水玻璃能显著加快水泥浆的凝结时间。凝胶时间随水玻璃浓度和水灰比等因素的变化而变化。当水泥浆较稠时,随着水玻璃浓度的增加,抗压强度增加;水泥浆浓度处于中间状态时,抗压强度基本没什么变化;水泥浆较稀时,随着水玻璃浓度的增加,抗压强度是降低的。     

基于差异时效的服务评价模型

针对KNN(k-nearest neighbor)方法在服务评价过程中存在的时效量化、评价窗口宽度以及反馈控制等问题,提出了具有差异时效的服务评价模型(WSEM-VTU).在WSEM-VTU中,采用系统动力学方法研究复杂时效量化方法,以获得差异时效量化结果;基于复杂时效量化方法得到的结果,自适应计算评价窗口宽度;根据评价统计特征,设计恶意评价反馈控制策略.通过实验,将WSEM-VTU与现有评价模型WSEM-E及WSEM-KNN进行比较,结果表明:WSEM-VTU的平均误差为0.877,比WSEM-E和WSEM-KNN分别降低了1.020和0.135;引入反馈控制策略后,WSEM-VTU出现恶意评价的情况平均降低67%.     

先进出行信息下事故对行程时间可靠性的影响

定义路径行程时间可靠性为在交通事故期间内平均路径行驶时间小于事故前路径出行时间乘以可接受拥堵水平的概率,由此导出路网行程时间可靠性.假定事故持续时间服从正态分布并将研究时域划分成相同的时段,在先进出行信息下,利用元胞传输模型进行路段流量加载,给出了每一个时段内路径行程时间的递推式,并在每一个时段内更新1次路径出行时间,出行者根据更新的出行时间运用Logit模型进行路径决策,最后基于Monte-Carlo法模拟求解路网行程时间可靠性.算例结果表明,行程时间可靠性随事故持续时间和方差及需求的增加而减小;可靠性随可接受拥堵水平的增加而增加;在拥堵网络中,包含事故路段的OD间需求越高,可靠性越低.