基于守恒高阶模型和支持向量机的多车道车辆换道模型

针对高速公路车辆换道问题, 提出一个多车道车辆换道模型。利用支持向量机(SVM)在多维特征下二分类问题的优势, 将SVM和Lagrange坐标下的高阶守恒模型(CHO)结合, 通过全离散跟车模型生成原始数据, 采用SMOTE(Synthetic Minority Oversampling Technique)算法对数据进行预处理, 采用双指标评估度SVM进行训练, 建立多车道车辆换道仿真模型。仿真结果表明: 基于支持向量机和CHO模型的换道模型, 驾驶车能够就当前的驾驶环境, 准确地作出决策, 有效地模拟高速公路上真实的多车道驾驶情况。     

多车种LWR交通流模型的半离散中心迎风格式

对多车种LWR交通流模型,给出一种半离散中心迎风格式,该格式以五阶WENO-Z重构和半离散中心迎风数值通量为基础.WENO-Z重构方法的引入提高了格式的精度,并保证格式具有基本无振荡的性质.时间的离散采用保持强稳定性的Runge-Kutta方法.通过数值算例验证了格式的有效性.     

交通流双车跟驰模型与数值仿真

基于全速度差(FVD)模型,考虑双前车信息的影响,提出了交通流双车跟驰模型.通过线性稳定性分析,得到了改进模型的稳定性条件. 与FVD模型对比研究表明,改进模型的稳定区域有明显增加.数值模拟结果表明,改进模型通过调节次近邻前车信息,可以避免FVD模型中因为反应系数较小时出现负速度的缺陷.同时也表明次近邻前车对交通流存在不可忽视的影响. 关键词： 交通流 双车跟驰模型 模拟     

两车道交通流耦合格子模型与数值仿真

考虑两车道耦合效应的影响和换道效应,提出了改进的两车道交通流耦合格子模型.同时,改进了换道时的流量转移率,这样更符合实际交通情况.通过线性稳定性分析,得到了改进模型的稳定性条件.数值模拟结果也表明,模型通过考虑耦合作用信息,更好地再现了换道情况,同时也表明两车道间的耦合效应对两车道交通流存在不可忽视的影响.     

准滑模控制应用于行人通道的交通瓶颈

单向行人交通在瓶颈处极易发生堵塞,设置障碍会阻碍行人通行,却能保证瓶颈处通畅.本文将准滑模控制引入LWR模型中,理论推导控制变量.在三车道宏观交通流模型基础上,模拟瓶颈处的障碍对行人流的影响,解释设置障碍的作用.研究表明准滑模控制变量的设置与阈值有关,当阈值对应基本图的流量最大点时,行人流量能保持最大值,达到提高单向行人交通瓶颈处的行人流通过效率. 关键词： 交通瓶颈 准滑模控制 LWR模型     

基于改进单高斯模型法的交通背景提取

在分析交通背景提取的特点和单高斯模型法的不足的基础上,提出了一种改进的单高斯模型法.该算法利用均值法初始化背景模型,引入判断值进行背景更新,运用邻域判别法实现干扰点抑制.不同天气条件,不同交通场景下的实验表明：与单高斯模型法相比,改进算法在提取背景时可以随车辆的走停及时更新,并且抑制了非静止背景物体的干扰.     

考虑驾驶员预估效应的交通流格子模型与数值仿真

考虑驾驶员的预估效应对车流的影响,提出了一个改进的一维交通流格子模型.基于线性稳定性理论得到了该模型的线性稳定性判据;运用非线性分析方法导出了描述交通阻塞相变时的mKdV方程.应用数值仿真验证了mKdV方程的解,研究表明适当考虑车流中预估效应的作用能够增强交通流稳定性,从而能有效抑制交通阻塞的形成. 关键词： 预估效应 交通流 格子模型 数值仿真     

一维确定性主干道交通流模型的研究

利用一维确定性元胞自动机模型模拟主干道交通流 ,研究绿信比、道路上车辆密度、周期、交通灯的多少对主干道交通流流量和流速的影响     

弯道交通流跟驰建模与稳定性分析

为发现弯道道路条件下交通流的演化特性及稳定条件,在全速度差最优模型的基础上,研究了弯道情况下的交通流跟驰模型表达式,并以状态空间法和控制系统的稳定性判据对其进行了稳定性分析,得到弯道情况下交通流的稳定条件,最后进行了数值模拟,验证了模型稳定条件的正确性.研究结果表明在相同的初始交通流密度情况下,弯道道路随着道路弧度的增大,交通流稳定性逐渐降低.     

智能交通环境下多信息反馈效应的跟驰模型研究综述

智能交通环境下,驾驶员可实时获取前方多辆车行驶状态信息,并根据反馈信息合理调整自身车辆运行状态,以期实现前后多车协同行驶的目标。本文简要回顾了跟驰模型近70年的发展历程,重点聚焦理论驱动跟驰模型,系统梳理了智能交通环境下多信息反馈效应跟驰模型的相关研究,给出了不同信息反馈作用下各模型的具体表征及其特性,归纳出多信息反馈效应跟驰模型动力学方程的一般形式。最后展望了该模型未来的发展趋势,为进一步完善跟驰理论及模型提供有益的参考。     

Incident Detection Method of Expressway Based on Traffic Flow Simulation Model

The expressway traffic incidents have the characteristics of high harmful, strong destructive and refractory. Incident detection can guarantee smooth operation of the expressway, reduce traffic congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffic ratio at the entrances and crossway in the network. The expressway traffic simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffic network states so as to provide advice for the highway management department.     

Study on the Reduced Traffic Congestion Method Based on Dynamic Guidance Information

This paper studies how to generate the reasonable information of travelers' decision in real network. This problem is very complex because the travelers' decision is constrained by different human behavior. The network conditions can be predicted by using the advanced dynamic OD(Origin-Destination, OD) estimation techniques. Based on the improved mesoscopic traffic model, the predictable dynamic traffic guidance information can be obtained accurately.A consistency algorithm is designed to investigate the travelers' decision by simulating the dynamic response to guidance information. The simulation results show that the proposed method can provide the best guidance information. Further,a case study is conducted to verify the theoretical results and to draw managerial insights into the potential of dynamic guidance strategy in improving traffic performance.     

Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework

Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not or insufficiently considered. Furthermore, the performance of the models has not been fully evaluated and compared in the traffic forecasting context, rendering the choice of the models dilemmatic for traffic volatility modeling. In this study, an omnibus traffic volatility forecasting framework is proposed, where various traffic volatility models with symmetric and asymmetric properties can be developed in a unifying way by fixing or flexibly estimating three key parameters, namely the Box-Cox transformation coefficient λ, the shift factor b, and the rotation factor c. Extensive traffic speed datasets collected from urban roads of Kunshan city, China, and from freeway segments of the San Diego Region, USA, were used to evaluate the proposed framework and develop traffic volatility forecasting models in a number of case studies. The models include the standard GARCH, the threshold GARCH (TGARCH), the nonlinear ARCH (NGARCH), the nonlinear-asymmetric GARCH (NAGARCH), the Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and the family GARCH (FGARCH). The mean forecasting performance of the models was measured with mean absolute error (MAE) and mean absolute percentage error (MAPE), while the volatility forecasting performance of the models was measured with volatility mean absolute error (VMAE), directional accuracy (DA), kickoff percentage (KP), and average confidence length (ACL). Experimental results demonstrate the effectiveness and flexibility of the proposed framework and provide insights into how to develop and select proper traffic volatility forecasting models in different situations.     

A New Macro Model for Traffic Flow on a Highway with Ramps and Numerical Tests

In this paper, we present a new macro model for traffic flow on a highway with ramps based on the existing models. We use the new model to study the effects of on-off-ramp on the main road traffic during the morning rush period and the evening rush period. Numerical tests show that, during the two rush periods, these effects are often different and related to the status of the main road traffic. If the main road traffic flow is uniform, then ramps always produce stop-and-go traffic when the main road density is between two critical values, and ramps have little effect on the main road traffic when the main road density is less than the smaller critical value or greater than the larger critical value. If a small perturbation appears on the main road, ramp may lead to stop-and-go traffic, or relieve or even eliminate the stop-and-go traffic, under different circumstances. These results are consistent with real traffic, which shows that the new model is reasonable.     

远程LXI测试业务流建模与仿真研究

针对探索利用现有通信网络科学规划和优化远程测试系统的问题,提出了一种以测试任务要求为中心的测试业务流建模及其仿真实现的方法。在分析远程测试系统通信特性及其测试业务流建模仿真流程的基础上,给出了基于吸引系数法的各模块间业务流矩阵,并对交互式业务、数据传输业务和网络背景业务分别建模。最后利用OPNET仿真软件给出了远程测试通信网运用的仿真实例,实现了利用仿真法定量分析和明确远程测试系统承载测试任务强度的技术途径。     

Modeling and Simulation for Urban Rail Traffic Problem Based on Cellular Automata

Based on the Nagel-Schreckenberg model, we propose a new cellular automata model to simulate the urban rail traffic flow under moving block system and present a new minimum instantaneous distance formula under pure moving block. We also analyze the characteristics of the urban rail traffic flow under the influence of train density, station dwell times, the length of train, and the train velocity. Train delays can be decreased effectively through flexible departure intervals according to the preceding train type before its departure. The results demonstrate that a suitable adjustment of the current train velocity based on the following train velocity can greatly shorten the minimum departure intervals and then increase the capacity of rail transit.     

Modeling and Simulation for Urban Rail Traffic Problem Based on Cellular Automata

Based on the Nagel-Schreckenberg model, we propose a new cellular automata model to simulate the urban rail traffic flow under moving block system and present a new minimum instantaneous distance formula under pure moving block. We also analyze the characteristics of the urban rail traffic flow under the influence of train density, station dwell times, the length of train, and the train velocity. Train delays can be decreased effectively through flexible departure intervals according to the preceding train type before its departure. The results demonstrate that a suitable adjustment of the current train velocity based on the following train velocity can greatly shorten the minimum departure intervals and then increase the capacity of rail transit.     

A Lattice Hydrodynamic Model for Traffic Flow Accounting for Driver Anticipation Effect of the Next-Nearest-Neighbor Site

In this paper, a new lattice hydrodynamic model is proposed by incorporating the driver anticipation effect of next-nearest-neighbor site. The linear stability analysis and nonlinear analysis show that the driver anticipation effect of next-nearest-neighbor site can enlarge the stable area of traffic flow. The space can be divided into three regions： stab/e, metastable, and unstable. Numerical simulation further illuminates that the driver anticipation effect of the next-neaxest-neighbor site can stabilize tramc flow in our modified lattice model, which is consistent with the analytical results.