大跨刚构桥最大悬臂阶段驰振稳定性的数值风洞研究

驰振是一种钝体截面的细长结构在空气中的气动不稳定现象,其振动频率较低。大跨刚构桥最大悬臂施工阶段由于结构较柔,存在发生驰振的可能性,因此必须研究该状态下的驰振稳定性。驰振分析通常借助风洞试验进行,试验周期长,费用高。基于计算流体动力学的数值风洞方法,首先求解不可压雷诺时均N—S方程,获得桥梁主梁截面的气动力特性,然后通过求解结构驰振运动方程,以评价大跨刚构桥最大悬臂施工阶段的驰振稳定性。用于贵阳小关连续刚构桥最大悬臂施工阶段横风向驰振稳定性分析,研究表明该桥在最不利施工状态不会发生驰振。     

一种新型的势垒式限滑差速器

提出了一种新型势垒式限滑差速器。分析了这种差速器的工作原理,介绍了齿轮设计所采用的非渐开线齿形并沿齿面节线分段设计的方法,进行了台架和路试的可靠性试验、最大牵引力试验和制动距离试验。     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.     

基于深度强化学习的车辆跟驰控制

针对自适应巡航控制系统在控制主车跟驰行驶中受前车运动状态的不确定性影响问题，在分析车辆运动特点的基础上，提出一种能够考虑前车运动随机性的跟驰控制策略。搭建驾驶人实车驾驶数据采集平台，招募驾驶人进行实车跟驰道路试验，建立驾驶人真实驾驶数据库。假设车辆未来时刻的加速度决策主要受前方目标车辆运动影响，建立基于双前车跟驰结构的主车纵向控制架构。将驾驶数据库中的驾驶数据分别视作前车和前前车运动变化历程，利用高斯过程算法建立了前车纵向加速度变化随机过程模型，实现对前方目标车运动状态分布的概率性建模。将车辆跟驰问题构建为一定奖励函数下的马尔可夫决策过程，引入深度强化学习研究主车跟驰控制问题。利用近端策略优化算法建立车辆跟驰控制策略，通过与前车运动随机过程模型进行交互式迭代学习，得到具有运动不确定性跟驰环境下的主车纵向控制策略，实现对车辆纵向控制的最优决策。最后基于真实驾驶数据，对控制策略进行测试。研究结果表明：该策略建立了车辆纵向控制与主车和双前车状态之间的映射关系，在迭代学习过程中对前车运动的随机性进行考虑，跟驰控制中不需要对前车运动进行额外的概率预测，能够以较低的计算量实现主车稳定跟随前车行驶。     

Investigation of ship waves in a narrow channel

This paper presents a discussion of the characteristics of ship waves in a narrow channel restricted by vertical walls, based on observed data and the results computed by a 2-D model. In the numerical model, the propagation of waves generated by a moving ship is simulated by solving 2-D depth-integrated Boussinesq equations. To get the boundary conditions at the location of the ship, the slender-ship approximation is employed. A field observation was carried out at a straight length of navigation channel. The ships targeted in the observations are two kinds of waterbuses with lengths of 28 and 24 m. The relative depth Froude number for the river current, an appropriate parameter for assessing the influence of the current on ship wave characteristics in a navigation channel, ranged from 0.47 to 0.76. The observed maximum wave height varied between 0.13 and 1.26 m. The maximum wave height of the wave train is sharply increased when the relative depth Froude number exceeds 0.6. The results computed by the present model agree fairly well with the observed data.     

A new estimation approach for the multiple discrete–continuous probit (MDCP) choice model

This paper develops a blueprint (complete with matrix notation) to apply Bhat’s (2011) Maximum Approximate Composite Marginal Likelihood (MACML) inference approach for the estimation of cross-sectional as well as panel multiple discrete–continuous probit (MDCP) models. A simulation exercise is undertaken to evaluate the ability of the proposed approach to recover parameters from a cross-sectional MDCP model. The results show that the MACML approach does very well in recovering parameters, as well as appears to accurately capture the curvature of the Hessian of the log-likelihood function. The paper also demonstrates the application of the proposed approach through a study of individuals’ recreational (i.e., long distance leisure) choice among alternative destination locations and the number of trips to each recreational destination location, using data drawn from the 2004 to 2005 Michigan statewide household travel survey.     

基于高斯过程机器学习方法的隧道围岩分类模型

针对现有围岩分类方法的局限性,基于工程实例,利用分类性能优异的高斯过程机器学习模型建立围岩类别与其主要影响因素之间的非线性映射关系,进而提出一种基于高斯过程的隧道围岩分类模型,实现不同情况下围岩分类的合理识别.将该模型应用于川藏公路二郎山隧道围岩分类,研究结果表明,隧道围岩分类的高斯过程机器学习模型是科学可行的,与人工神经网络模型、支持向量机模型相比较,该模型具有参数自适应化的优点,能方便快捷地给出合理可靠且具有概率意义的围岩分类评价结果,可对围岩分类结果的不确定性或可信度进行定量化评价.     

数字高斯白噪声的频域和时域特性分析

分析了数字高斯白噪声在频域的频谱特性和在时间域的统计特性,提出在数字处理速度一定时,随着输出噪声频域带宽的增加,输出噪声的时间统计特性不再符合高斯分布.通过增加滤波器阶数可以提高噪声的时间统计特性,但是将增加输出噪声的带内波动.通过计算机仿真验证了上述的理论分析,并给出了频域和时域特性都满足相应要求的带宽范围实验值.     

基于核函数变换的NLPCC分析在洪水分类中的应用

通过拟线性化变换和降维去噪,得出多维分类指标的低维主成分值,然后通过对每一待分类样本的低维主成分值进行聚类分析,最终得出洪水的自然分类结果.洪水分类实例结果表明,该计算方法不需要复杂的计算机专业知识和优化算法知识,原理清楚,计算简单,结果客观有效,不失为一种洪水分类评价的新途径.选择适宜的非线性变换函数是正确应用该方法的关键,同时对于能够预先给出分类指标值且数值范围较小时,需要指标标准数值判定和聚类效果判定相结合.