城市主干道调头口设置探讨

随着城市的不断发展,城市主要干道中调头需求已成为交通流中不可忽视的一部分。该文通过对北京市通州区城市主干道现况调头口的运行状况进行调查研究,分析车辆调头对交叉口交通功能的影响程度,就如何合理地设置调头口,得出初步的结论,从设计和管理的角度提出建议以供参考。     

基于遗传算法和极限学习机的智能算法在基坑变形预测中的应用

为解决传统智能算法网络结构参数复杂、运算速度慢等问题,基于遗传算法和极限学习机构建基坑变形的新型优化智能预测模型。先利用皮尔逊相关系数评价不同影响因素与基坑沉降变形之间的相关性,以确定极限学习机的输入层; 再采用试算法确定最优激励函数和隐层节点数,并将遗传算法和极限学习机耦合,利用遗传算法优化极限学习机的初始权值和阈值,以提高预测精度。经实例检验表明： 1)开挖时间、开挖深度、土体抗剪参数及重度均与基坑沉降变形显著相关,为构建极限学习机输入层提供了依据; 2)在预测过程中,激励函数和隐层节点数对极限学习机的预测效果具有一定的影响,以Sigmiod型激励函数和13个隐层节点数的预测效果为最优; 3)通过遗传算法的优化,能进一步提高预测精度,验证了遗传算法的优化能力和有效性。预测模型在不同工况下的预测结果均较优,说明该模型具有较高的稳定性和可靠性。     

基于概率模型的交叉口感应信号控制研究

考虑多种因素影响下初始绿灯时间和车辆到达服从概率分布特征,提出了1种交叉口感应控制最大绿灯时间优化模型,以交叉口平均延误、停车次数、排队长度为评价指标,利用 Vissim 仿真软件,将该模型与传统感应控制模型进行对比。仿真结果表明提出的算法在不同的饱和度下改进效果比较明显,尤其当交通量较大、饱和度较高时,传统感应控制效果有限,采用该模型的改进效果依然显著。     

基于EHPN的A-SMGCS机场滑行道运行控制建模

针对先进机场场面引导与控制系统(A-SMGCS)中滑行道运行控制建模, 提出一种基于扩展层次Petri网(EHPN)的建模方法。将滑行道分为交叉口和直线段模块, 然后对模块内部的航空器行为进行封装, 形成基于扩展Petri网的过程层通用模块, 并进一步采用同步合成技术建立过程层模型。利用面向Agent的着色赋时Petri网建立决策层通用模块, 同时将场面运行控制知识与模块的库所和变迁集成, 增强模型的智能决策能力。采用某机场12 h滑行道运行数据进行了仿真试验。研究结果表明: 基于EHPN模型的仿真, 其直线段和交叉口冲突次数分别为6次和11次, 滑行道系统总延误为346 s, 与SIMMOD仿真模型效果相近, 因此, 该建模方法有效。     

交通路口可变相位信号控制

利用集合理论的一些相关知识, 研究了孤立交通路口的信号控制, 提出了孤立交通路口可变相位信号控制的方法。与传统交通路口模糊信号控制的方法相比, 可变相位信号控制的相位顺序、相位时间以及相位组合更加灵活, 更适应交通路口实时变化的交通状况, 同时可变相位信号控制能够适应不同形状交通路口的信号控制。用TSIS仿真软件仿真结果表明可变相位信号控制的效果要优于传统模糊控制, 是进行路口信号控制的一种实用和有效的算法。     

城市灯控平面交叉口的自行车交通设计研究

城市灯控平面交叉口混合交通流安全和效率的核心研究对象应该是自行车与机动车交通流间的相互影响。本丈在大量的理论分析和实践的基础上,探讨时空分离法的自行车交通设计的基本原则和交通空间设计方法,即自行车右转弯专用车道设计、左转自行车二次过街以及自行车与行人过街一体化设计,并进行了南京市太平北路与珠江路交叉口的应用实例分析。本文的城市灯控平面交叉口自行车的交通设计方法可为解决城市灯控平面交叉口混合交通问题提供借鉴。     

基于灰局势理论的交叉口安全设计综合决策模型

道路信号交叉口作为城市交通网络的重要节点,因交通运行复杂,是交通事故的集中发生地.为了降低交叉口事故发生的概率,并综合平衡各方面因素,科学地进行交通安全设计尤为重要.在多个设计方案比选过程中,综合决策模型的优劣直接决定了交通安全改善设计的实施效果.应用灰局势决策理论,选取交通事故折减系数(CRF)、工程费用、对绿化的影响、交叉口延误4项指标,结合专家打分确定各指标的权重,建立交通安全设计综合决策模型,并以实际交叉口的交通安全设计为例进行实证性研究.     

基于集合经验模态分解的滚动轴承振动信号希尔伯特谱分析方法

  目的  提出一种从强背景噪声、非平稳、非线性的复杂设备滚动轴承早期冲击故障振动信号中有效提取故障特征并进行故障模式识别的方法。  方法  首先,利用快速谱相关（FSC）分析提取原始振动信号的故障特征,并利用多尺度排列熵（MPE）对故障特征进行量化;然后,将故障特征数据输入BP神经网络进行故障诊断模型训练与测试;最后,对变速情况下的滚动轴承故障模拟实验数据和美国凯斯西储大学公开的轴承故障试验数据集进行故障识别研究。  结果  结果显示：所提方法对不同类型的故障具有较高的辨识精度,可达97%以上。  结论  研究验证了基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法的可行性和优越性,可为滚动轴承健康状态评估提供技术支持。     

The symmetric intersection design and traffic control optimization

It is well recognized that the left-turning movement reduces the intersection capacity significantly, because exclusive left turn phases are needed to discharge left turn vehicles only. This paper proposes the concept of Left-Hand Traffic (LHT) arterial, on where vehicles follow left-hand traffic rules as in England and India. The unconventional intersection where a LHT arterial intersects with a Right-Hand Traffic (RHT) arterial is named as symmetric intersection. It is only need three basic signal phases to separate all conflicts at symmetric intersection, while it at least need four signal phases at a conventional intersection. So, compared with the conventional intersection, the symmetric intersection can provide longer green time for the left-turning and the through movement, which can increase the capacity significantly. Through-movement waiting areas (TWAs) can be set at the symmetric intersection effectively, which can increase the capacity and short the cycle length furthermore. And the symmetric intersection is Channelized to improve the safety of TWAs. The Binary-Mixed-Integer-Linear-Programming (BMILP) model is employed to formulate the capacity maximization problem and signal cycle length minimization problem of the symmetric intersection. The BMILP model can be solved by standard branch-and-bound algorithms efficiently and outputs the lane allocation, signal timing decisions, and other decisions. Experiments analysis shows that the symmetric intersection with TWAs can increase the capacity and short the signal cycle length.     

A copula-based approach for estimating the travel time reliability of urban arterial

Estimating the travel time reliability (TTR) of urban arterial is critical for real-time and reliable route guidance and provides theoretical bases and technical support for sophisticated traffic management and control. The state-of-art procedures for arterial TTR estimation usually assume that path travel time follows a certain distribution, with less consideration about segment correlations. However, the conventional approach is usually unrealistic because an important feature of urban arterial is the dependent structure of travel times on continuous segments. In this study, a copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification. First, segments correlation is empirically analyzed and different types of copula models are examined. Then, fitting marginal distributions for segment TTD is conducted by parametric and non-parametric regression analysis, respectively. Based on the estimated parameters of the models, the best-fitting copula is determined in terms of the goodness-of-fit tests. Last, the model is examined at two study sites with AVI data and NGSIM trajectory data, respectively. The results of path TTD estimation demonstrate the advantage of the proposed copula-based approach, compared with the convolution model without capturing segments correlation and the empirical distribution fitting methods. Furthermore, when considering the segments correlation effect, it was found that the estimated path TTR is more accurate than that by the convolution model.