A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

Estimating travel time functions for urban roads: options and issues

Travel time functions specify the relationship between the travel time on a road and the volume of traffic on the road. Until recently, the parameters of travel time functions were rarely estimated in practice; however, a compelling case can be made for the empirical examination of these functions. This paper reviews, and qualitatively evaluates, a range of options for developing a set of travel time functions. A hierarchy of travel time functions is defined based on four levels of network detail: area, corridor, route and link. This hierarchy is illustrated by considering the development of travel time functions for Adelaide. Alternative sources of data for estimating travel time functions are identified.

In general, the costs and benefits increase as the travel time functions are estimated at finer levels of network detail. The costs of developing travel time functions include data acquisition costs and analysis costs. The benefits include the potential for reducing prediction errors, the degree of application flexibility and the policy sensitivity of the travel time functions.     

基于OD反推的公交站点团OD矩阵预测

城市公共交通的公交站点数量多,在实际操作中,预测站点之间的客流OD分布往往非常困难,因此需把与主要客流集散点相近的站点合并成公交站点团,通过预测站点团的OD矩阵来掌握客流量的分布情况,从而为合理布置公交线网、站场提供科学依据。     

多特征点驱动的船舶轨迹聚类方法

轨迹聚类在船舶行为分析与海事监管等领域发挥着重要作用。船舶轨迹存在长度与采样率不一致、结构差异明显等特点,在大范围水域难以实现大量船舶轨迹的高精度与快速聚类。针对该问题,在利用船舶自动识别系统获取海量船舶历史航行数据的基础上,提取与船舶航行行为、船舶交通密度相关的位置特征点,进而提出了多特征点驱动的船舶轨迹聚类方法。针对船舶航行时在大多数情形下具有保向、保速的特点,采用数据压缩的方法捕获船舶航行状态以及船舶航向发生显著变化的轨迹点,作为船舶轨迹结构特征点;针对目标水域中某些特定区域常存在船舶交叉会遇的情形,利用概率密度估计法分析船舶交通流的空间分布特点,并提取船舶会遇局面下的轨迹点,作为船舶交通流特征点;为剔除2类特征点中的异常值,采用密度聚类算法对特征点进行聚类,进一步提高特征点提取的可靠性,并将聚类结果中每类特征点的中心作为代表性特征点;统计途经代表性特征点的船舶轨迹分布情况,将具有相似分布的船舶轨迹视为同一类。实验结果表明：相比于常用的K-medoids聚类、层次聚类、谱聚类和DBSCAN等方法,提出的轨迹聚类方法在成山头水域、长江口南槽水域及舟山水域等典型区域均可获得优异的聚类结果;在上述典型水域,平均轮廓系数分别提升约53%,71%,63%和41%,戴维森堡丁指数分别降低约57%,67%,63%和45%;同时,此方法可平均降低约56%的聚类时间,显著提升了船舶轨迹数据聚类分析的效率。     

悬链线构件的有限元方法

悬链线构件在船舶与海洋工程中是广泛存在的一种结构,无论是船舶码头旁靠、锚泊定位中的系泊线,还是各种系泊结构物中各种深海悬链线结构(如深海柔性立管、深海锚泊系统、复合材料悬链线立管等)的系泊力响应,对整个结构物都显得非常重要。其中,由于悬链线构件具有很强的几何非线性,其受力分析的精度对其运动响应的分析尤为重要。根据悬链线构件静力分析,得到悬链线单元的柔度矩阵,从而进一步推导出悬链线单元的刚度矩阵和单元节点力向量,最后以商业有限元软件ABAQUS的用户自定义单元(UEL)为工具,开发出悬链线单元。结果显示:运用此单元得到的简单悬链线结构的数值解与解析解完全一致,同时也基本符合实验结果,为解决各种复杂悬链线构件问题的直接计算法提供了一种新的思路。

     

非自由交通流的non-FIFO车辆轨迹估计算法

将三角基本图的交通状态细分为自由流、中断和拥堵;基于非自由流特性,重新划分了U型时空域,以此找到适合的波速范围;重新确定了上游边界累积流量,使得边界函数刻画不过于宽泛;建立了非自由流Newell模型,并提出了使用该模型的判断条件;引入了车辆秩参数,达到在多车道上描述车辆超车现象的目的,并建立了更精确的车辆秩估计模型,从而建立了非自由流Newell扩展模型;提出了针对非自由流下2种情形的车辆轨迹估计算法,根据是否存在超车现象分为先进先出(FIFO)情形与非先进先出(non-FIFO)情形;结合数值模拟和实际交通案例,验证了算法的有效性。研究结果表明：2种情形下的轨迹估计算法都是有效的,当超车现象存在时,non-FIFO情形的估计效果较准确和稳健;在数值模拟研究中,non-FIFO情形的估计误差相对FIFO情形下降13.45%,non-FIFO情形更优;实际交通案例中,2个小汽车数据集在non-FIFO情形的估计误差相对FIFO情形均有所下降,下降幅度分别为2.38%、2.04%,且估计误差均服从高斯混合模型;公交车数据集因不存在超车现象,non-FIFO与FIFO情形的估计误差相等,均为4.90%,且估计误差服从伽马分布。可见,所建立的非自由流Newell模型对于中断多或拥堵状态占比多的交通数据均是有效可行的,且所提出的non-FIFO和FIFO情形的轨迹估计算法效果表现良好。     

A Constrained Controller Based on State Estimation

IntroductionIn practice, owing to the limitations of actua-tors and other physical properties, the inputs of asystem are often subject to constraints. It has sig-nificant theoretical and engineering values to inves-tigate the control of constrained systems. In thepast years, many attempts have been made on thisissue[1-5]. For a class of uncertain constrained dis-crete-time systems, Kothare, et al[4]proposed anonline receding predictive control algorithm. Byanalyzing the degradation condition …     

混合动力发动机在线扭矩估算算法研究

为了满足混合动力发动机扭矩管理控制策略的需要,建立了基于平均值模型和A/N—Te模型的发动机在线扭矩估算算法。在试验过程中,针对进气歧管压力响应滞后于节气门快速变化的问题,提出了预估算进气歧管压力估计进气流量的方法,消除了进气歧管内压力滞后的影响,完善了原始算法。通过台架试验得到发动机输出扭矩实测值,将实测扭矩与估算扭矩相比较,验证了算法的准确性。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.