全风化千枚岩复合改良土路用性能

为了充分利用全风化千枚岩作为路基填料,设计了红黏土掺和比分别为0、20%、40%、60%和100%,水泥掺量分别为0、3%和5%的组合改良方案,开展了改良土的界限含水率、抗剪强度和无侧限抗压强度试验,分析了改良土的路用性能。试验结果表明：当水泥掺量分别为3%与5%时,复合改良土的液限均低于40%,符合路基设计中液限低于40%的控制要求;改良土的黏聚力随红黏土掺和比与水泥掺量的增大而增大,内摩擦角随红黏土掺和比的增长先增大后减小,随水泥掺量的增大而增大,但两指标在水泥掺量大于3%时增长幅度较小。改良土路基极限承载力计算结果表明：5%水泥改良全风化千枚岩路基极限承载力仅为725.3 kPa,红黏土掺和比为40%改良全风化千枚岩路基极限承载力达到2 198.3 kPa,分别是全风化千枚岩路基承载力的2.34和7.10倍,因此,红黏土改良效果优于水泥;经过比较可得红黏土掺和比为40%,水泥掺量为3%是合理掺和方案,在28 d养护后,路基极限承载力计算值为4 247.7 kPa,液限为32.7%。微观机理分析结果表明：红黏土颗粒小于全风化千枚岩颗粒,当红黏土掺和比大于40%时可以包围千枚岩颗粒的点-点接触,增加了接触点数与接触面积,从而大大提高了改良土路基的极限承载力。无侧限抗压强度试验结果表明：优化方案改良土7 d无侧限抗压强度为487.25 kPa,满足铁路路基设计要求。     

受电弓与接触网系统方案设计方法及其应用

为提高弓网系统设计效率和系统固有可靠性,需开展弓网系统方案设计方法研究。具体方法为:根据弓网系统设计目标、弓网动态性能评价依据和接触网技术参数的设计步骤及依据,筛选接触网初步设计方案,再通过弓网动力学仿真计算结果和技术经济性比较,确定最终的弓网系统设计方案。应用本设计方法,设计满足SSS400+和CX-GI型两种双弓运行速度为380 km/h的弓网系统建议方案。通过以上研究分析和工程应用,形成一套科学合理的弓网系统方案通用性设计方法。     

高铁隧道穿越不同岩性接触带施工方法研究

随着我国高速铁路在西部地区的大力发展,铁路隧道经常需要穿越不同岩性接触带地层。隧道在穿越不同岩性接触带的施工过程中,易出现围岩塌方、变形过大、支护开裂等问题。以银西高铁贾塬隧道穿越不同岩性接触带为研究背景,采用FLAC^3D软件模拟隧道分别采用三台阶法,三台阶七步法,三台阶核心土法,三台阶临时仰拱法穿越红黏土与砂岩夹泥岩接触带,分析4种工况下围岩位移、围岩应力和支护应力的变化规律,并结合现场监测数据验证,比选出最优工法。研究结果表明:隧道穿越红黏土与砂岩夹泥岩接触带时,上下两种地层围岩位移相差较大;采用不同工法施工时,围岩应力和支护应力分布规律不同;采用三台阶临时仰拱法和三台阶七步法能够有效控制围岩位移;采用三台阶预留核心土法和三台阶七步法施工时支护受力更加均匀。     

三维弹塑性轮轨滑动接触热机耦合分析

为提高轮轨滑动接触热响应分析的准确性,基于Johnson-Cook材料模型,充分考虑含摩擦因数在内多种材料属性的温度相关性、3种热传递方式和轮轨实际廓形,建立了全比例三维弹塑性轮轨滑动接触有限元模型,采用完全耦合法对滑动接触状态下的轮轨进行热机耦合分析;研究了车轮以1 m·s^-1速度沿钢轨滑行0.1 s时的轮轨温度场和应力场分布特性,分析了轴重、相对滑动速度对轮轨接触区温度场的影响,得到了热影响层深度、热影响层宽度、轮轨表层温度随轴重、相对滑动速度的变化关系。分析结果表明：轮轨最大等效应力发生在次表层接触斑中心处,车轮表层最高温度发生在接触斑后半部分中心处,车轮表层最高温度为848 ℃,钢轨表层最高温度为768 ℃,钢轨表层最高温度低于车轮表层最高温度;轮轨热影响层很薄,车轮热影响层深度约为4.22 mm,钢轨热影响层深度约为3 mm;轮轨热影响层深度随轴重增大无明显变化,而宽度随轴重的增大而增大,轮轨热影响层深度随相对滑动速度的增大而减小,而宽度随相对滑动速度增大无明显变化,轮轨表层温度随轴重和相对滑动速度的增大而增大,且相对滑动速度对轮轨热响应影响更大。全比例三维弹塑性轮轨滑动接触有限元模型及热机完全耦合法能够更加准确地预测轮轨滑动接触热响应,对合理开展轮轨热损伤和热疲劳研究具有重要意义。     

海上无人系统集群发展现状及关键技术研究进展

针对欠驱动无人艇（USV）自主回收的需求,提出一种基于动态规划制导的跟踪控制方法。

在运动学层面,将平行接近制导（CB）与动态窗口算法（DWA）相结合,引导USV实现目标跟踪与动态避障;在动力学层面,针对模型参数及回收环境的不确定性,采用径向基函数神经网络（RBFNN）设计动力学滑模控制器,实现对制导输出的跟踪控制。随后,采用Lyapunov理论对系统的稳定性进行分析。

仿真结果表明,所提方法使USV具备了稳定跟踪性能,可有效规避回收过程中的动态障碍,并能自适应估计模型中的不确定因素和未知的环境干扰。

所提方法展现出较强的鲁棒性与灵活性,可为USV在动态环境下的回收进行制导与目标跟踪提供参考。

     

星型数据网络通信仿真分析

就专网中的星形数据网推导出数学排队模型,并进行计算机仿真,得到相应的网络性能参数,对星形数据网的运行具有指导性意义。     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

A dynamic linear model for the estimation of time‐varying origin–destination matrices from link counts

We propose a dynamic linear model (DLM) for the estimation of day‐to‐day time‐varying origin–destination (OD) matrices from link counts. Mean OD flows are assumed to vary over time as a locally constant model. We take into account variability in OD flows, route flows, and link volumes. Given a time series of observed link volumes, sequential Bayesian inference is applied in order to estimate mean OD flows. The conditions under which mean OD flows may be estimated are established, and computational studies on two benchmark transportation networks from the literature are carried out. In both cases, the DLM converged to the unobserved mean OD flows when given sufficient observations of traffic link volumes despite assuming uninformative prior OD matrices. We discuss limitations and extensions of the proposed DLM. Copyright © 2017 John Wiley & Sons, Ltd.     

A dynamic behavioural traffic assignment model with strategic agents

Foresee traffic conditions and demand is a major issue nowadays that is very often approached using simulation tools. The aim of this work is to propose an innovative strategy to tackle such problem, relying on the presentation and analysis of a behavioural dynamic traffic assignment.The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time already spent in his journey.The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents’ strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large and not previously explored networks.Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against existing approaches and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results in terms of accuracy and computational efficiency. This indicates that the proposed approach has the potential to provide better tools to investigate and forecast drivers’ choice behaviours. Eventually these tools can improve the delivery and efficiency of traffic information to the drivers.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.