Modeling and optimizing traffic light settings in road networks

We discuss continuous traffic flow network models including traffic lights. A mathematical model for traffic light settings within a macroscopic continuous traffic flow network is presented, and theoretical properties are investigated. The switching of the traffic light states is modeled as a discrete decision and is subject to optimization. A numerical approach for the optimization of switching points as a function of time based upon the macroscopic traffic flow model is proposed. The numerical discussion relies on an equivalent reformulation of the original problem as well as a mixed-integer discretization of the flow dynamics. The large-scale optimization problem is solved using derived heuristics within the optimization process. Numerical experiments are presented for a single intersection as well as for a road network.     

基于高斯混合一时间序列模型的轨迹预测

针对不同时间道路车流量变化下轨迹预测误差变化大的问题,提出基于概率分布模型的高斯混合-时间序列模型（GMTSM）,对海量车辆历史轨迹进行模型回归和路段车流量的分析以实现车辆轨迹预测。首先,针对均匀网格划分方法容易造成相关轨迹点分裂的问题,提出迭代式网格划分来实现轨迹点的数量均衡;其次,训练并结合高斯混合模型（GMM）和时间序列分析中的差分自回归滑动平均模型（ARIMA）;然后,为了避免GMTSM中子模型自身的不稳定性对预测结果产生干扰,对子模型的预测进行误差分析,动态计算子模型的权重;最后,依据动态权重组合子模型实现轨迹预测。实验结果表明,GMTSM在路段车流量突变情况下,平均预测准确率为90.3%;与相同参数设置下的高斯混合模型和马尔可夫模型相比,GMTSM预测准确性提高了55%左右。GMTSM不仅能在正常情况下准确预测车辆轨迹,而且能有效提高道路车流量变化情况下的轨迹预测准确率,适用于现实路况环境。     

Discovering traffic congestion through traffic flow patterns generated by moving object trajectories

The discovery of moving object trajectory patterns representing high traffic density has been covered in various works using diverse approaches. These models are useful in areas such as transportation planning, traffic monitoring, and advertising on public roads. However, though studies tend to recognize the importance of these types of patterns in utility, they usually do not consider traffic congestion as a particular condition of high traffic. In this work, we present a model for the discovery of high traffic flow patterns in relation to traffic congestion. This relationship is represented in terms of traffic that is shared between different sectors of the pattern, making it possible to identify traffic flow situations causing congestion. We also complement this model by discovering alternative paths for the severe traffic depicted in these patterns. These alternative paths depend on traffic level and location inside the road network. Depending on the traffic conditions, alternative paths are commonly sought by drivers when they are approaching a traffic jam, in order to mitigate the effects of traffic congestion. We compare these models with related work from similar areas and validate them by conducting experiments using real data. We describe discovered patterns related to the main elements of the road network in the dataset and show their advantages in comparison to related models. Based on the displayed metrics, the algorithms’ implementation offers good performance execution for the given dataset volume. The results presented confirm the usefulness of the proposed patterns as a tool that helps to improve traffic, allowing the identification of problems and possible alternatives.     

基于元胞自动机的交通流计算机模拟

元胞自动机是把复杂系统量化为简单的个体,在元胞自动机模型中.空间、时间都被离散化,每一个相互作用的单元仅为有限的状态.以元胞自动机理论为基础,把车辆在路段上交通流中运动的变化规律表述为元胞自动机的演化规则,建立了基于元胞自动机理论的交通流模拟模型,标定了元胞长度和最大速度等参数,分析了元胞变换的原则;详细探讨了元胞自动机在道路交通模拟中的应用,设计出了交通元胞自动机的结构,分析了交通元胞自动机所采取的状态变换原则,建立了一维(单车道)交通流模拟模型;并利用C语言编程实现模拟.模拟结果符合实际交通流的特点.     

大数据环境下移动对象自适应轨迹预测模型

已有的轨迹预测算法针对移动对象运动模式,使用数学模型进行交通流模拟,难以对路网中的移动对象进行准确的描述.为了解决这一问题,提出基于隐马尔可夫模型(hidden Markov model,简称HMM)的自适应轨迹预测模型SATP(self-adaptive trajectory prediction model based on HMM),对大数据环境下移动对象海量轨迹利用基于密度的聚类方法进行位置密度分区和高效分段处理,减少HMM的状态数量.根据输入轨迹自动选取参数组合,避免HMM模型中隐状态不连续、状态停留等问题.实验结果表明,SATP模型在实验中表现出较高的预测准确性,并维持较低的时间开销.针对速度随机改变的移动对象,其平均预测准确率为84.1%;相同情况下,平均高出朴素预测算法46.7%.     

Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation

A continuous adjoint approach for obtaining sensitivity derivatives on unstructured grids is developed and analyzed. The derivation of the costate equations is presented, and a second-order accurate discretization method is described. The relationship between the continuous formulation and a discrete formulation is explored for inviscid, as well as for viscous flow. Several limitations in a strict adherence to the continuous approach are uncovered, and an approach that circumvents these difficulties is presented. The issue of grid sensitivities, which do not arise naturally in the continuous formulation, is investigated and is observed to be of importance when dealing with geometric singularities. A method is described for modifying inviscid and viscous meshes during the design cycle to accommodate changes in the surface shape. The accuracy of the sensitivity derivatives is established by comparing with finite-difference gradients and several design examples are presented.     

城市交通网络宏观控制模型及仿真实现

为研究适合城市交通网络控制系统应用的交通流预测模型,在改进Van Den Berg, M的路段交通流模型的基础上,建立了以路口交通流为基本建模单元,以动态非线性离散方程反映交通流变化的城市交通网络宏观模型.为验证该模型能有效地预测城市路网的交通流信息,在VC++net环境下,开发了城市交通宏观控制模型仿真系统UTFS,设计了网络拓扑结构模块,以适应不同规模、不同复杂程度的实际交通网络的仿真要求.最后选取典型网络进行应用研究.仿真结果表明:该模型满足交通控制对控制模型的实时性和精度要求,该仿真系统可以作为城市交通网络宏观控制模型验证的有效工具,也可以作为城市交通控制系统控制和优化研究的辅助工具.     

机器人视觉跟踪轨迹的分级规划方法

针对跟踪轨迹规划对于确保得到连续光滑的跟踪运动的重要性,提出了两级视觉跟踪轨迹规划方法。第一阶段在图像平面上规划运动轨迹,在图像平面上得到的离散规划点映射到机器人关节空间。第二阶段在机器人关节空间中用三次样条函数来连接这些离散点。为了满足实时控制的要求,在图像处理过程中采用窗口技术并抽取边缘特征。建立用于跟踪两维平面运动物体(如随运输带运动的物体)的机器人视觉跟踪控制系统。实验结果表明,跟踪误差渐近地减小到允许的数值范围,所提出的跟踪轨迹规划方法是有效的。     

航空器轨迹预测技术研究综述

航空器轨迹预测是流量管理、冲突检测和解脱、航空器进场排序以及异常行为监测等空中交通管理技术的基础。关于航空器轨迹预测的研究产生了许多经典的方法和应用领域。对研究航迹预测问题的背景和意义进行概述,并从数据库、基础流程和预测关键技术三个方面介绍了有关航迹预测的基础知识。其中数据库包括航空器性能数据库、航空器监视数据库和气象数据库,基础流程包括准备、预测、更新和输出四个模块,预测关键技术总结并列举了状态估计模型、动力学模型和机器学习模型三类方法的典型模型。对航迹预测系统模型进行具体分析时,进一步列举三类方法的主要研究成果并归纳各类方法的特点。对航迹预测在空中交通管理中的具体应用进行分析,包括冲突检测、到达管理和流量管理等。总结并指出了目前航迹预测问题所面临的挑战和未来的发展方向。     

HMM-Cluster：面向交通量过载发现的轨迹聚类方法

随着经济的发展,城市交通拥堵问题亟待解决,交通量过载发现是解决交通拥堵问题的有效方法之一。提出一种基于HMM模型的轨迹聚类算法HMM-Cluster,可有效地发现交通量过载情况。该算法首先提取时空轨迹特征点,并采用维数约简技术减少轨迹数据量,根据参照轨迹拟合HMM模型,基于密度函数得到轨迹相似度矩阵,最后给出聚合的相似性轨迹。真实轨迹数据集上的对比实验结果表明,提出的HMM-Cluster可有效地挖掘移动对象运动模式,准确发现交通量过载情况,具有一定实用价值。     

混合Petri网交叉口信号灯建模的模糊优化

城市车流量急剧增加和道路通行能力之间的矛盾日益激化,交通拥堵已成为亟待解决的社会问题。道路交叉口信号灯优化控制是解决该问题的有效方法。基于城市道路交通系统随机性强、离散性连续性混杂、难以用数学模型精确建模等特点,提出了一种用连续Petri网建立道路交通流模型,用离散Petri网对道路交叉口信号灯进行控制的方案。根据车流量的动态变化,采用模糊控制对交叉口绿灯时间进行自适应优化。仿真结果表明,该方案能提高交叉口通行能力,减少车辆延误,优于传统的控制方法。     

基于模糊着色Petri网的车辆运动轨迹预测

车辆运动轨迹的准确预测可以对潜在交通冲突进行实时有效预测,并为解决交通冲突提供最佳策略。以真车实验数据为基础,分析车辆运动轨迹的影响因素,建立车辆运动轨迹方程,根据坐标变换以及各个方程变量参数之间的转换关系,将车速纵向和横向加速度、车身侧倾角以及俯仰角速度作为模型输入,构建论域以及隶属度函数。此外,根据车辆不可能在较短时间内状态发生急剧变化建立相应的模糊规则,建立模糊着色Petri网模型,对车辆运动轨迹进行预测。以真实路车实验数据对模型进行训练与测试,测试结果表明该Petri网模型能够对短时长内的车辆运动轨迹进行有效预测。     

城市复杂道路网络交通流的分解模型

针对城市道路交通流非线性、不确定性和模糊性特点,将城市道路与快速干道作为整体对待,提出了面向控制应用的城市交通网络宏观动态离散模型。将城市街区作为划分基点,把整个城市道路复杂交通网络分解为交叉口和单向环形道路两个子系统,分别建立了它们的宏观动态模型。通过对交叉口进行理想虚拟变形,将各个单向环形道路连接在一起,从而形成各种复杂网络。对西安市中心区域的实际交通流数据进行了仿真研究,结果表明该交通流模型基本实现了城市道路与快速干道的统一分析建模,较好地反映了城市路网的交通流信息,可以作为城市交通控制系统分析和设计的有力工具。     

基于预测模型的轨迹数据压缩方法

针对目前路网环境下海量轨迹数据压缩效率低下的问题,提出了一种基于预测模型的轨迹数据压缩方法（CTPM）。通过将轨迹数据的时间信息和空间信息分别进行压缩,使得压缩后的轨迹数据在空间维度上无损,并且在时间维度上误差有界,以此提高压缩效率。在空间方面,首先利用部分匹配预测（PPM）算法通过轨迹已经行驶的部分路段对其下一时刻可能的位置进行预测;然后通过删除预测成功的路段来减少轨迹数据的存储代价。在时间方面,首先利用轨迹通行状况具有周期性的特点,构建了不同时间区间的通行速度统计模型,来预测移动对象进入下一路段所需要的时间;然后删除预测时间误差小于给定阈值的路段数据来进行压缩处理。实验结果显示,与已有的基于路网的并行轨迹压缩（PRESS）算法相比,CTPM的空间压缩比和时间压缩比平均分别提高了43%和1.5%,同时时间压缩误差减小了9.5%。实验结果表明所提算法在提高压缩比的同时有效地降低了压缩时间和压缩误差。     

Bounds on buffer overflow probabilities in communication systems

A model to analyze the buffer behaviour in a multiplexor is derived, based on the analogy between the buffer occupancy in a discrete time model of multiplexing and the waiting time of a GI/G/1 queueing system.The bounding techniques developed earlier by Kingman and Ross are extended to the discrete time model. Simple and useful bounds are obtained for the buffer overflow probabilities under general assumptions concerning incoming message traffic characteristics. Numerical examples are presented and compared with other methods.     

基于出租车GPS轨迹和POI数据的商业选址推荐

针对商业选址问题,提出一种基于城市出租车GPS轨迹和POI数据的商业选址推荐算法。首先,对城市出租车GPS轨迹和POI数据进行预处理及地图匹配,然后将城区进行交通小区划分,用OD矩阵分析交通小区之间的交通流量特征,并结合交通小区内POI的分布特征和语义属性,构建基于OD矩阵和对应小区POI数据相结合的商业地址推荐模型。最后,应用兰州市出租车GPS轨迹与POI数据验证了推荐算法的有效性和实用性,并将推荐结果在交通小区尺度上进行可视化呈现。实验结果表明,该推荐算法不仅能够推荐合理的商业选址,为商业选址决策提供快速有效的可视化定量分析方法,同时能够为城市公共服务设施空间布局规划提供决策依据。     

Discrete event simulation case study: Diagnostic path for stroke patients in a stroke unit

Hospitals aim at delivering efficient and effective healthcare. This requires high quality medical care, provided by highly trained and motivated professionals. Also the way operations are organized can contribute to a smoother healthcare delivery. An important point here is patient flow and waiting times during the patient trajectory diagnosis-therapy-care. Due to the large variability that exist between patients care needs, even for patients with a similar pathology, predicting the waiting times is very challenging. Traditional analytical optimization models like queuing theory cannot cope with the complexity of the pathways followed by patients and the interrelationships between the resources they need. Discrete event simulation can help to provide insight into the impact of operational changes, e.g. concerning available scanner capacity, on the timing of the patient’s trajectory in a hospital unit.This paper focuses on the diagnostic part of the stay of stroke patients in a stroke unit of a university hospital. With the help of discrete event simulation this patient flow is analyzed and the impact of potential changes in the capacity profile of test resources is investigated. The paper describes the steps taken in the study and the challenges met. The structure of the simulation program is explained and the results of the scenario analysis are discussed. The paper starts with a brief exploration of the use of discrete event simulation in healthcare and ends with general observations on the subject.     

Constrained multi-objective trajectory planning of parallel kinematic machines

This paper presents a new approach to multi-objective dynamic trajectory planning of parallel kinematic machines (PKM) under task, workspace and manipulator constraints. The robot kinematic and dynamic model, (including actuators) is first developed. Then the proposed trajectory planning system is introduced. It minimizes electrical and kinetic energy, robot traveling time separating two sampling periods, and maximizes a measure of manipulability allowing singularity avoidance. Several technological constraints such as actuator, link length and workspace limitations, and some task requirements, such as passing through imposed poses are simultaneously satisfied. The discrete augmented Lagrangean technique is used to solve the resulting strong nonlinear constrained optimal control problem. A decoupled formulation is proposed in order to cope with some difficulties arising from dynamic parameters computation. A systematic implementation procedure is provided along with some numerical issues. Simulation results proving the effectiveness of the proposed approach are given and discussed.     

利用相空间轨迹产生混沌脉冲序列的方法研究

在混沌理论的基础上,提出了利用具有伪随机性的混沌相轨迹产生混沌脉冲序列的方法．利用连续混沌系统生成了混沌脉冲序列,解决了利用一般离散混沌系统所产生的混沌序列模型简单、周期短、数字退化、易跟踪等问题．该方法产生的混沌序列应用于保密通信中,可以提升混沌密钥的伪随机性,实现无限长周期．仿真结果证明了本文提出方法的可行性．     

考虑换道意图的LSTM-AdaBoost车辆轨迹预测模型

不合理的车辆的换道行为是导致交通事故发生的主要原因之一,提前预知换道车辆的轨迹并及时做出相应调整有助于减少事故的发生。针对换道车辆轨迹预测问题,采用将深度学习和集成学习相结合的轨迹预测方法,并考虑了换道意图的影响。建立连续隐马尔可夫模型对车辆进行换道意图检测,提前判别车辆的换道状态,并输入至相应的轨迹预测模型中;将LSTM（long short term memory）作为AdaBoost算法（adaptive boosting）的基预测器,建立LSTM-AdaBoost模型,在多个基预测器同时进行轨迹预测的基础上,通过训练调整各个基预测器的权重并将结果加权集成,提升预测模型的精度和稳定性;通过NSGIM（next generation simulation）数据集对模型进行训练和测试,结果显示意图预测模型在变道前一秒的准确率在90%以上,LSTM-AdaBoost集成轨迹预测模型与单一的LSTM模型相比精度和稳定性显著提升,且预测结果中异常数据更少,具有较好的稳定性;同时预测对比结果也表明增加意图预测模块有助于提升换道轨迹预测的精度。