城市交通仿真中的随机数及其性能评价

针对城市交通系统仿真软件中广泛使用的线性同余法的不足,分别采用图形逼近和统计检验方法,比较目前使用的和正在研究的随机数发生器。根据范数理论,给出2-范数意义下的统计检验性能评价表达式和误差。分析结果表明,在交通仿真中,采用MT算法可以取得较优性能。通过实例证明MT算法较好逼近了城市交通的真实状态。     

随机数发生器探讨及一种真随机数发生器实现

高质量随机数在信息安全中具有重要的应用。该文利用双重随机思想,基于真随机源对一个高质量伪随机数发生器进行参数重置,同时对该伪随机数发生器产生的随机数的特定二进制位进行采样从而获得0-1序列,将之填充到一个整数单元,归一化后作为输出。分析表明,该真随机数发生器具有良好的性能。     

动态数据驱动的交通仿真框架研究与实现

针对现有交通仿真中事先建立的理论模型难以准确预测交通状态发展趋势的问题,提出一种基于动态数据驱动应用系统范型的交通仿真框架。首先在微观仿真模型的基础上建立状态空间模型进行先验状态估计;继而将实测交通数据引入模型以调整和评估状态空间模型;基于交通状态非线性非高斯的特性,选用粒子滤波器,提出并实现了数据同化模型和相关算法,提炼传统粒子滤波器的关键步骤并对其进行改进,以提高状态估计的能力;最后基于微型交通仿真软件MovSim实现了上述框架。实验表明:基于该框架的交通状态预测精度得到明显提高,受测量误差和环境噪声的影响小,具有较强的预测稳定性和可靠性。     

基于MC的计算机仿真随机模型分析与设计

计算机仿真系统中经常要应用研究对象的随机分布模型获得符合要求的随机数,给出了利用蒙特卡罗(MC)法来近似地描述研究对象的概率分布的一般方法,设计了高质量的随机数发生器,研究了利用MC法得到计算机仿真系统中常见的随机分布模型算法,具有一定的实用价值.通过仿真实例,分析了在实际应用中应该注意的问题.     

ATmega128单片机的真随机数发生器

引言随机数已广泛地应用于仿真、抽样、数值分析、计算机程序设计、决策、美学和娱乐之中。常见的随机数发生器有两种：使用数学算法的伪随机数发生器和以物理随机量作为发生源的真随机数发生器。要获取真正随机的真随机数,常使用硬件随机数发生器的方法来获取。这些真随机数都是使基于特定的真随机数发生源（如热噪声、电流噪声等）,每次获取的真随机数都是不可测的,具有很好的随机性。真随机数因其随机性强,在数据加密、信息辅助、智能决策和初始化向量方面有着广泛应用,构建一种基于硬件真随机数发生源,具有广泛的应用价值。但目前硬件真随机数发生源均较复杂,而且很少有基于单片机的真随机数发生器。本文利用RC充放电的低稳定度,根据AVR单片机的特点设计了一种性价比极高的真随机数发生器。该随机数发生器使用元件很少,     

基于细菌觅食优化算法的城市轨道交通调度优化

为了合理高效地制定城市轨道交通调度方案,实现客流与车次的优化配置,提出了一种基于细菌觅食优化算法的城市轨道交通调度优化策略。兼顾乘客与运营企业双方利益,以发车间隔为决策变量,乘客平均候车时间最短和发车次数最少为优化目标,建立调度优化模型,并对细菌觅食优化算法求解该调度模型的过程进行分析。结合某城市轨道交通一号线实际运营数据进行仿真实验,并与其他算法的优化结果进行对比分析,实验表明该算法和模型能有效解决城市轨道交通调度优化问题。     

一种利用粒子滤波的实时交通状态估计方法

将粒子滤波理论和宏观随机交通流模型结合,对高速公路交通状态进行实时估计。在该方法中,高速公路被看作是由等距离的路段首尾相接而形成的模型,交通传感器通常设置在路段的交界处,而且数量远少于所需估计的交通状态。采用压缩状态空间的形式,将模型参数也作为交通状态而非常量进行估计。仿真结果表明粒子滤波方法能够有效地估计和跟踪交通状态的变化,并且与扩展卡尔曼滤波方法相比具有更高的精确度。     

基于粒子滤波的交通仿真系统

针对现有多数交通仿真系统在交通状态估计问题精确度不高的缺陷,引入粒子滤波方法对交通状态进行预测估计,设计并实现基于粒子滤波的交通仿真系统。首先介绍系统粒子滤波的初始化、粒子状态转移过程、权值计算及归一化和重采样操作的设计,然后将实测交通数据通过粒子滤波算法引入模型以提高状态估计的能力,最后基于微观交通仿真软件MovSim实现该系统。实验表明：基于粒子滤波的交通仿真系统具有较强的预测稳定性和准确性。     

正态分布真随机数云模型发生器

云模型发生器多是基于伪随机数的发生器.在充分研究了随机数发生器、云模型的国内外发展现状的基础上,设计了一种产生高质量随机数的硬件实现方案,并使用软件的方法将均匀分布的真随机序列逐步转换成正态分布真随机数一维云模型,全面论述了一种基于真随机数的云模型发生器的实现方法,国内尚属首次,是对云模型研究的有益补充.     

一种利用UKF的高速公路实时交通状态估计方法

把无轨迹卡尔曼滤波器（UKF）和宏观随机交通流模型结合在一起,可以实现对高速公路交通状态的实时估计。高速公路被看作是由等距离的路段首尾相接而成的系统,每个路段中交通变量的更新不光与其自身有关,还受到相邻路段的影响。交通传感器通常设置在路段的交界处,而且数量远少于所需估计的交通状态。采用压缩状态空间的形式,将模型参数也作为交通状态而非常量进行估计。仿真结果表明UKF方法能够有效地估计和跟踪交通状态的变化,并且与扩展卡尔曼滤波方法相比具有更高的精确度。     

几种随机相互作用势相关的交通流模型比较

为了研究随机因素对交通流的影响,人们提出了几种考虑顾前相互作用势的交通流模型.它们均基于简单非对称排斥过程和微观动力学模型,对于描述交通流的随机演化行为具有优越性.本文对几种势相关的交通流模型进行比较,特别是比较不同模型的车辆状态迁移的概率计算机制;进而通过数值仿真,比较不同模型刻画的随机演化现象.通过本文的比较,得到几种随机相互作用势相关的交通流模型的流量-密度图、时空斑图以及三维演化结果.为进一步优化势函数结构,建立符合实际交通的随机交通流模型奠定基础.     

基于元胞自动机的港湾公交站间距影响研究

公交系统对城市交通流的影响十分显著，在对现有经典交通流元胞自动机模型进行总体分析的基础上，提出了一种考虑公交车辆和港湾式公交停靠站影响的多速混合车辆双车道元胞自动机模型，重新标定了元胞长度、运行车速和随机慢化机制，引入了公交车停靠站，改进了车辆变换车道规则。通过计算机模拟，再现了传统交通流三参数之间的关系，为更好地描述城市混合交通流奠定了基础。     

Performance evaluation of an open distributed platform for realistic traffic generation

Network researchers have dedicated a notable part of their efforts to the area of modeling traffic and to the implementation of efficient traffic generators. We feel that there is a strong demand for traffic generators capable to reproduce realistic traffic patterns according to theoretical models and at the same time with high performance. This work presents an open distributed platform for traffic generation that we called distributed internet traffic generator (D-ITG), capable of producing traffic (network, transport and application layer) at packet level and of accurately replicating appropriate stochastic processes for both inter departure time (IDT) and packet size (PS) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. In this work a complete performance comparison among the centralized version and the two distributed versions of D-ITG is presented.     

Probabilistic Description of Traffic Flow

The study of traffic flow is investigated by different means. Well established theories are (i) kinematic models based on partial differential equations to describe traveling density waves, and (ii) deterministic models using nonlinear car‐following equations to determine trajectories of moving cars, as well as (iii) large-scale simulation hopping models like cellular automata. An important intermediate approach is (iv) the stochastic or probabilistic attempt to understand phenomena like “Stau aus dem Nichts” (phantom jam) on long crowded roads. Initiated by the old argument that road traffic is a stochastic process, we develop a new probabilistic theory based on Markov processes to improve our understanding of traffic flow and its three phases (free flow, synchronized motion, wide moving jams) discovered by Kerner. As an introductory example, first we consider a dissolution of a car queue described by the stochastic master equation as a one-step decay process. Furtheron more realistic models are developed to investigate the nucleation, growth and condensation as well as dissolution of car clusters on a circular one-lane freeway. In analogy to usual aggregation phenomena such as the formation of liquid droplets in supersaturated vapour the clustering behavior in traffic flow is described by the master equation. At overcritical densities the transition from the initial free particle situation (free flow of vehicles) to the final congested state, where one or several big aggregates of cars have been formed, is shown. In dependence on the concentration of cars on the road the stationary solution of the master equation is derived analytically. The obtained fundamental diagram as flow-density-relation indicates clearly the different regimes of traffic flow (free jet of cars, coexisting phase of jams and isolated cars, highly viscous heavy traffic). In the (thermodynamic) limit of infinite number of vehicles on an infinite long road the analytical solution for the fundamental diagram is in agreement with experimental traffic flow data. As a particular example we take into account measurements from German highways presented by Kerner and Rehborn.     

一种基于分类回归树的无人车汇流决策方法

决策规划是无人驾驶技术中的重要环节.由于道路结构变化或障碍物引起的车辆被动换道多采用基于逻辑规则或优化算法的决策方式.本文以通行量为优化目标,提出一种基于分类回归树（Classification and regression tree,CART）的汇流决策方法.依据交通流参数,选择大量具有代表性的车辆汇流场景.对场景中车辆的汇流决策序列进行编码,采用遗传算法搜索使得通行量最大的决策方案.将寻优获得的大量汇流决策序列作为样本,训练分类回归树.选取车辆自身信息及与周围车辆的关系等以描述环境特征,运用分类回归树描述环境特征与决策结果的映射关系,获得一种通行量最优的汇流决策方法.在软件中进行仿真实验,对比既有方法,基于分类回归树的汇流方法能够有效减少汇流行为对车流的扰动,在大流量情形下依旧能保持较高的通行效率.此外,该方法对实际实施中可能存在的环境感知误差,如定位误差,有一定的鲁棒性.     

A fast random number generator for stochastic simulations

A discrete random number (DRN) generator for stochastic differential equations is proposed. The generator has exactly 8 states and thus 10 DRN's can be obtained from a single 32-bit random variable. This is advantageous when large numbers of DRN's are needed, as for example in fluctuating lattice-Boltzmann models. The moments of the discrete distribution match those of a Gaussian distribution (zero mean and unit variance) up to 5th order. Numerical tests show that satisfactory statistical properties can be obtained with several 32-bit pseudo random number (PRN) generators.     

一类新的动态车辆调度问题的建模与算法

针对目前对于动态车辆调度问题的研究仅集中于考虑时间依赖或依概率变化的情形,在对原有动态车辆调度问题模型进行总结的基础上,综合考虑了时间依赖且网络依概率变化,以及结合带有时间窗和随机需求的情况,提出了新的问题模型,并提出求解该问题模型的多目标随机机会约束规划模型,设计了用遗传算法解决该模型的方案与步骤。实验结果表明,所提出的模型可有效地拟合交通状况,设计的算法可以有效地求解该模型。     

Traffic flow control in automated highway systems

The problem of vehicle traffic flow control in automated highway systems is analyzed. Given desired vehicular density and velocity profiles along a highway, stabilizing velocity controllers are designed so that the real vehicle traffic flow converges to the desired profiles. The controllers are derived using vehicle conservation flow models and Lyapunov stability techniques. A highway topology with a discrete number of lanes is considered. Vehicles can have different destinations and perform diverse maneuvers. The control law obtained is distributed and therefore is suited for implementation on AHS hierarchical multilayer architectures. Simulation results are presented.     

Modeling stochastic live load for long-span bridge based on microscopic traffic flow simulation

A general framework of modeling the stochastic live load from traffic for a long-span bridge is developed. The cellular automaton (CA) traffic flow simulation technique is adopted to develop the analytical basis of the framework. Based on the traffic flow simulation results, the live load on a long-span bridge from the stochastic traffic is studied with a focus on the static component. Parametric studies of major variables of the framework, such as the length of the connecting roadways, the speed limit, and the vehicle combination, are conducted.     

FPGEN: A fast, scalable and programmable traffic generator for the performance evaluation of high-speed computer networks

Testing today’s high-speed network equipment requires the generation of network traffic which is similar to the real Internet traffic at Gbps line rates. There are many software-based traffic generators which can generate packets according to different stochastic distributions. However, they are not suitable for high-speed hardware test platforms. This paper describes FPGEN (Fast Packet GENerator), a programmable random traffic generator which is entirely implemented on FPGA (Field Programmable Gate Array). FPGEN can generate variable packet sizes and traffic with Poisson and Markov-modulated on-off statistics at OC-48 rate per interface. Our work that is presented in this paper includes the theoretical design of FPGEN, the hardware design of the FPGA-based traffic generator board (printed circuit board design and construction) and the implementation of FPGEN on FPGA. Our experimental study demonstrates that FPGEN can achieve both the desired rate and statistical properties for the generated traffic.