基于神经网络的MUSIC计算方法

MUSIC方法是空间谱估计中经典的子空间方法,这类算法有个共同特点就是要对输出数据的协方差矩阵进行数学分解,其计算量较大,不适合实时处理。因此,文章提出了基于神经网络的高效迭代方法,不需进行数学分解,计算过程相对简单。仿真结果证明了该方法的有效性。     

盾构始发与到达设计与施工决策分析系统研制

为了控制盾构施工的风险和提高盾构设计与施工的决策效率,采用资料分析、理论计算和软件开发的方法,对盾构始发与到达施工决策相关基础与应用问题进行了全面研究。得到以下研究成果:1)提出盾构始发和到达的风险分类,并以此为基础,研究了不同加固方案下盾构始发与到达施工存在的风险问题;2)通过对端头地层稳定性的研究,提出端头地层稳定性分析与判别方法,构建始发与到达加固方案知识库;3)建立一个包含100个工程的案例数据库,为决策系统提供了数据库支撑;4)基于工程案例数据库,研制具有地层稳定性判别、加固方法选择、加固范围确定、反力架支撑验算、风险分析、施组设计标准化等功能的盾构始发与到达设计与施工决策辅助系统,有助于工程设计与施工方案的科学分析与决策。     

盾构始发与到达端头地层加固方法选择与稳定性评价

盾构法作为当前城市软土隧道最常用的修建方法之一,相关工程事故却屡见不鲜,究其原因,多是由于端头地层的稳定性未能得到有效控制所致,因此,端头地层失稳成为盾构始发和到达施工的一个主要风险点。为降低该风险,保证盾构工程的顺利进行,基于相关工程资料和研究成果的总结分析,结合作者的工程设计经验,针对盾构始发与到达的端头地层稳定性加固控制问题,通过研究得到以下成果:1)提出了一个盾构始发与到达的加固方案库,并以此为基础,通过程序构建了一套盾构始发与到达加固方案的自动推理方法;2)考虑有水和无水2种情况,根据各类土的类型特性和关键参数指标,对盾构始发与到达工程中的各单一类型土层分别进行了稳定性分析与判别;3)根据各类土的地层稳定性分析与判断结果,提出了盾构始发与到达地层的综合稳定性判别方法。     

低温BMS系统剩余充电时间预估的研究

当前新能源汽车大规模使用,充电时间过长是影响车主使用的主要问题,而剩余充电时间预估不准又给车主带来另外的烦恼,特别是低温状态下充电预估时间,因低温加热影响又增加了预估时间难度。本文介绍一种低温BMS系统剩余充电时间预估的策略,并结合真实的充电柜和温度箱进行验证。     

基于全数检验与定时截尾试验的地铁列车整车可靠性验收方法

提出地铁列车可靠性验收的基本条件,分别介绍了基于定时截尾试验、序贯截尾试验和全数检验的3种经典的可靠性验收方法;提出基于全数检验和定时截尾试验的地铁列车整车可靠性验收方法,设计了可靠性验收的流程;以某一具体项目为例,对该可靠性验收方法进行了验证。结果证明:基于全数检验与定时截尾试验的地铁列车整车可靠性验收方法是有效的,可以节省试验时间。     

川藏铁路跨线列车合理跨线时间范围分析

本文基于旅客出行时间特征,将跨线列车划分为日间动车组、跨夜动车组、日间普通旅客列车、跨夜普通旅客列车等四类。结合跨线列车的定义,推导出跨线列车的适应性条件,确定其影响因素为线上运行时间、线下运行时间、全程旅行时间。跨线列车的适应性条件可以确定跨线列车的种类、初始跨线时间域。结合跨线列车的始发终到时间域、跨线时间域,推导出跨线列车的合理始发终到时间范围、合理跨线时间范围的计算方法,并实例求解了川藏铁路跨线列车的合理跨线时间范围,验证了跨线列车的适应性判别条件、合理始发终到时间范围、合理跨线时间范围求解方法的可行性与正确性。研究结论可以为川藏铁路列车运行图的铺画、分时段客货共线的研究提供参考。     

诱导信息下城市轨道交通乘客路径选择行为研究

为研究城市轨道交通诱导信息对乘客的影响，考虑信息提供时间、方式、更新频率及其与 路径属性之间的交互关系，提出融合诱导信息的城市轨道交通乘客路径选择行为决策框架。考 虑乘客在决策时表现出遵循混合决策规则的倾向差异和不同属性感知差异，建立改进的混合效用-后悔模型。将路径属性与诱导信息同时纳入调研情景设计，基于调研结果标定模型中关键参数，结果表明，基于混合决策规则的模型拟合效果最高(调整优度比达0.396)。进一步分析乘客对信息的偏好：相较于下载手机APP，乘客更喜欢社交媒体推送的形式接收诱导信息；引入信息推送频率时间价值，并对比得出，女性、中老年及非通勤乘客更倾向于接受更高频率的诱导信息推送服务；分析各属性弹性值，表明诱导信息提供方式和更新频率应作为管理者的辅助手段，在特定场景下(例如高峰拥堵和突发事故)诱导信息将会发挥更大的作用。研究揭示了诱导信息提供方式如何影响轨道交通乘客的路径决策机理，有助于设计更加精准和高效的诱导信息策略辅助客流组织管理。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

Application of Lagrangian relaxation approach to α-reliable path finding in stochastic networks with correlated link travel times

This study investigates the important problem of determining a reliable path in a stochastic network with correlated link travel times. First, the distribution of path travel time is quantified by using trip records from GPS probe vehicles. Second, the spatial correlation of link travel time is explicitly considered by using a correlation coefficient matrix, which is incorporated into the α-reliable path problem by Cholesky decomposition. Third, the Lagrangian relaxation based framework is used to handle the α-reliable path problem, by which the intractable problem with a non-linear and non-additive structure can be decomposed into several easy-to-solve problems. Finally, the path-finding performance of this approach is tested on a real-world network. The results show that 15 iterations of calculation can yield a small relative gap between upper and lower bounds of the optimal solution and the average running time is about 5 s for most OD settings. The applicability of α-reliable path finding is validated by a case study.