山区公路驾驶员交通心理与客运安全研究

针对山区驾驶员的交通安全心理,从道路环境、车、驾驶员三方面建立交通安全感影响指标集,利用层次分析法和灰色加权关联法对山区复杂公路客运驾驶员的安全感进行映射性评价.评价结果显示,影响驾驶员安全感的前8位指标分别是驾驶员操作水平、职业习惯、道路类型、驾驶员性格、心理稳定性、交通复杂程度、规范操作行为和行驶平均车速.应用此评价模型可以对驾驶员进行科学干预和学习培训,从而预防和减少客运交通事故.对秦岭山区商洛市的研究表明,此方法适用于区域客运驾驶员的交通安全感综合评价,也适用于固定客运班线的驾驶员评价.     

FDPSO-TLD运动补偿的仿真研究

基于ADAMS软件对干树采集模式的FDPSO中张力腿甲板(TLD)与船体之间的运动补偿问题进行研究,根据张力腿甲板的概念设计建立舷外和舷内配重两种张力系统模型,提出模型简化方法,对两种模型在不同工况下甲板的动力响应和补偿效果进行比较。     

A pedestrian flow model considering the impact of local density: Voronoi diagram based heuristics approach

Local density, which is an indicator for comfortable moving of a pedestrian, is rarely considered in traditional force based and heuristics based pedestrian flow models. However, comfortable moving is surely a demand of pedestrian in normal situations. Recently, Voronoi diagram had been successfully adopted to obtain the local density of a pedestrian in empirical studies. In this paper, Voronoi diagram is introduced into the heuristics based pedestrian flow model. It provides not only local density but also other information for determining moving velocity and direction. Those information include personal space, safe distance, neighbors, and three elementary characteristics directions. Several typical scenarios are set up to verify the proposed model. The simulation results show that the velocity-density relations and capacities of bottleneck are consistent with the empirical data, and many self-organization phenomena, i.e., arching phenomenon and lane formation, are also reproduced. The pedestrians are likely to be homogeneously distributed when they are sensitive to local density, otherwise pedestrians are non-uniformly distributed and the stop-and-go waves are likely to be reproduced. Such results indicate that the Voronoi diagram is a promising tool in modeling pedestrian dynamics.     

A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting

The k-nearest neighbor (KNN) model is an effective statistical model applied in short-term traffic forecasting that can provide reliable data to guide travelers. This study proposes an improved KNN model to enhance forecasting accuracy based on spatiotemporal correlation and to achieve multistep forecasting. The physical distances among road segments are replaced with equivalent distances, which are defined by the static and dynamic data collected from real road networks. The traffic state of a road segment is described by a spatiotemporal state matrix instead of only a time series as in the original KNN model. The nearest neighbors are selected according to the Gaussian weighted Euclidean distance, which adjusts the influences of time and space factors on spatiotemporal state matrices. The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that the improved KNN model is more appropriate for short-term traffic multistep forecasting than the other models are. This study also discusses the application of the improved KNN model in a time-varying traffic state.     

区域城际轨道交通网络与空间结构适应性

为城际轨道网布局的适应性提供适用的决策方法,在分析城际轨道交通网络与区域空间结构适应性内涵的基础上,建立了既能反映区域城际轨道交通网络和区域空间结构主要特征,又能反映二者相关联的适应性评价指标体系.提出城市群城际轨道网布局的适应性评价模型——基于灰色关联熵的加权灰色关联度评价模型.该模型以加权灰色关联评价模型作为研究框架,对平均灰色关联度模型进行改进,应用灰熵分析方法确定关联系数的权重,更真实全面地反映了城际轨道网布局与区域空间结构的适应性情况.以武汉都市圈城际轨道交通线网规划为例,验证该方法的合理性和有效可行性.     

基于WPCA的港口用地调整交通影响评价

确定面向港口用地功能调整包括港区内、外两部分评价指标体系,采用灰色关联度确定指标的权重,建立基于加权主成分分析法的港口用地调整交通影响评价模型。在天津港的应用表明,该评价方法能把影响范围内的各项指标综合起来,最大限度地减少评价中的主观成分,为类似港口和物流园区规划建设提供了决策支持,验证了该方法的有效性。     

基于离差最大化原理的航班延误风险分级研究

基于航班延误历史数据统计分析与数据挖掘技术,利用一种新的离差最大化客观赋权法对延误指标进行客观赋权,结合主观层次赋权法,对延误指标进行主客观加权赋权;最后运用多等级模糊综合评价法对航班环中的航班延误进行风险分级,使延误分级更合理。研究结果表明,航空延误的有效分级可以控制延误传播范围,使延误在局域得到解决方案。     

面内流固冲击载荷下初缺陷矩形薄板的动力屈曲

取样条函数作为挠度试函数,运用加权残值法求得初缺陷矩形薄板动力屈曲的控制方程,采用四阶Runge-Kutta法求解该控制方程,并用Fortran语言编制了相应的计算程序。构造的B样条函数能适应板侧边上的任意弹性转动约束,讨论了初始几何缺陷、冲击载荷持续时间和弹性约束的影响。     

基于Kalman滤波器的故障诊断方法及其在铁道车辆中的应用

现结合Kalman滤波器和统计的方法对铁道车辆悬挂系统故障进行诊断。为了避免高频引起的数值振荡，这里提出采用求导和降阶的方法得到关于加速度及其导数的状态方程来代替通常的关于位移及其速度的状态方程。通过改变二系垂向阻尼系数来模拟故障，数值结果表明，该方法能有效诊断铁道车辆垂向悬挂系统的故障。     

基于随机加权自适应容积卡尔曼的电池SOC估计

准确估计锂离子电池荷电状态（SOC）对于突破电动汽车发展瓶颈，推动电动汽车商业化至关重要。针对动力电池模型参数辨识问题，提出基于遗忘因子的递推最小二乘法（FRLS）的模型参数在线识别方法。实时测量动力电池电流和电压数据，在线辨识模型参数并实时更新，实时反映电池内部参数的变化过程，对电池动态特性进行实时模拟。针对容积卡尔曼（CKF）滤波过程中对噪声敏感的问题，提出一种基于随机加权思想的自适应容积卡尔曼滤波（ARWCKF）方法。相比于常规CKF容积点权值始终不变，通过引入随机加权因子，自适应调整容积点权值并对系统噪声、状态向量及观测向量进行预测，抑制系统噪声对状态估计的干扰，避免因容积点权重值固定所带来的误差。针对CKF算法在容积点计算过程中由于状态方差矩阵失去正定性导致的平方根分解无法使用的问题，提出基于奇异值分解的容积点计算方法，克服由于先验协方差矩阵负定性变化而导致的滤波精度下降等问题，并进行多种工况、温度下不同SOC初值的对比验证。结果表明：所提出的基于遗忘因子的递推最小二乘法的在线参数辨识及ARWCKF滤波方法具备良好的估计精度及收敛能力，最大电压估计误差不超过40 mV，SOC估计误差不超过1%。