A shockwave profile model for traffic flow on congested urban arterials

In this paper a new traffic flow model for congested arterial networks, named shockwave profile model (SPM), is presented. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves: queuing, discharge, departure, and compression waves. Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solutions, SPM treats each homogeneous road segment with constant capacity as a section; and the queuing dynamics within each section are described by tracing the shockwave fronts. SPM is particularly suitable for simulating traffic flow on congested signalized arterials especially with queue spillover problems, where the steady-state periodic pattern of queue build-up and dissipation process may break down. Depending on when and where spillover occurs along a signalized arterial, a large number of queuing patterns may be possible. Therefore it becomes difficult to apply the conventional approach directly to track shockwave fronts. To overcome this difficulty, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new smaller cycles, so that the analytical solutions for tracing the shockwave fronts can be easily applied. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly reduces the computational load and improves the numerical efficiency. We further validated SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate the effectiveness and accuracy of the model. We expect that in the future this model can be applied in a number of real-time applications such as arterial performance prediction and signal optimization.     

Real time queue length estimation for signalized intersections using travel times from mobile sensors

We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.     

Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data

With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper.     

数字通信信号调制识别的Gabor变换方法

Gabor变换是线性时频变换中的一种，它通过加窗函数的办法对Fourler变换加以改进。把Gabor变换引入到了数字通信信号调制识别领域，对ASK、FSK和PsK信号的Gabor变换域特征进行了理论分析和软件仿真，最后给出了识别算法和仿真结果。     

数字信号接地电阻的自动监测技术的探讨

针对我国目前舰用数字信号接地系统的现状，提出了数字信号接地电阻的自动监测技术，并给出了信号接地电阻的自动监测系统的原理框图、软件流程图及其硬件组成。     

卫星通信地球站干扰条件下性能仿真与分析

文章分析了地球站在干扰情况下系统误码率计算方法,谈论了卫星通信中主要的干扰信号样式,并给出了几种常见干扰信号的表达式和功率谱密度,然后利用MATLAB/Simulink建立了卫星通信干扰模型,分别仿真了单音信号,多音信号和脉冲信号干扰地球站接收系统。通过仿真分析,得到了三种干扰信号对地球站的干扰效果,对地球站抗干扰具有指导意义。     

基于细胞自动机方法的车行运动模型及信号灯控制策略研究

采用细胞自动机方法研究了在单向道路上行驶的2辆不同性能(加速度能力、最大速度等)车辆的运动状态和相互关系,以及性能参数对行驶状态的影响.讨论了在设置路口和信号灯的情况下,不同信号灯控制策略(信号灯的时间周期及绿信比)对车辆运动状态的影响.细胞自动机方法通过JAVA编程进行数值模拟,得到车辆行驶时间-位置之间的关系,结果与预期相同.该模型退化为匀速运动状态情形时,所得结果与近期文献的结果相一致,也证明了文中所用方法的可靠性和合理性.     

公交信号优先控制策略研究综述

公交信号优先是提高公交系统运行速度和可靠性的重要手段。回顾公交信号优先控制40多年的研究成果,以总结该领域的总体研究脉络。对被动优先、主动优先、实时优先以及与不同设施相结合的信号优先控制策略进行了综述分析。研究表明,公交信号优先控制策略的发展历程是：控制的实时性逐步提高,优化要素的考虑逐渐全面,控制对象日益扩大,控制策略逐步系统化、适用性逐步增强。最后指出,公交信号优先控制多目标平衡、控制策略的协调与网络优先控制,以及控制与调度策略的协调优化是后续研究的重点,而公交车辆行程时间预测以及如何应对预测偏差带来的影响仍然是信号优先控制中的关键问题。     

基于Rao-Blackwellised粒子滤波的岩石声发射信号去噪

针对声发射数据采集过程易受随机噪声干扰,提出了Rao—Blackwellised粒子滤波的信号降噪方法。在地震波模型的基础上,建立了声发射信号的状态空间模型。由于两者在某些方面有相通性,从而把降噪问题转为在状态空间模型下的滤波问题。首先,通过对构造信号的仿真实验,验证Rao—Black—wellised粒子滤波能够用于信号滤波;其次,利用Rao—Blackwellised粒子滤波,对石灰岩声发射信号进行降噪处理,并取得较好的滤波效果,从而为处理声发射数据提供一条新途径。     

铁路车站信号设备故障诊断专家系统的研究

针对我国铁路信号设备故障的特点,基于诊断和专家系统的理论与方法,对铁路车站信号设备故障诊断专家系统的原型开发结构进行设计分析.通过对铁路车站信号设备的常见故障进行具体分析,从构造知识库,到模型推理,为建立切实可行的专家系统进行详尽的阐述,为通过人工智能方法建立和开发我国铁路信号故障诊断专家系统提供重要依据.