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.     

都市行车引导系统的模型研究（下）

4.2模型分析 将网络中的流分组,每组的大小由一个时间段内进入网络的流所确定。每组用第一辆车和最后一辆车的运行来仿真,用事故出现的频率、受影响的路段以及事故出现时间等参数仿真事故,当仿真事故时,路段容量发生变化。     

交通诱导预测控制仿真研究

将动态交通分配实施过程纳入预测控制框架下以满足实时交通诱导的目的,提出一种交通诱导预测控制算法．该算法是在滚动时域基础上进行的,包括实时交通分配、交通流模拟运行及评价以及进化最佳路径３ 个重要环节．仿真结果表明,交通诱导预测控制是一种良好的计算机控制方法学,其优化过程预先考虑了目前交通分配对未来路网的影响,因而可有效地防范交通拥堵,实现考虑反馈的路网交通流实时分配优化,同时为出行者提供最佳路径．     

基于小波变换和极限学习机的网络流量预测模型

为了提高网络流量预测准确性,结合网络流量的变化特点,针对当前网络流量预测模型存在的局限性,设计了基于小波变换和极限学习机的网络流量预测模型。首先分析了当前国内外网络流量预测研究现状,找到引起网络流量预测准确性差的原因;然后采用小波变换对原始网络流量时间序列进行去噪,得到无噪声的网络流量时间序列;最后采用极限学习机对网络流量时间序列进行建模,得到相应的预测结果。与当前经典的网络流量预测模型在相同环境下进行对照测试,测试结果分析表明,小波变换和极限学习机的网络流量预测精度达到了95%以上,网络流量预测误差得到了有效的控制,而且提升了网络流量预测效率,预测结果要远优于当前经典的网络流量预测模型。     

对网络QoS中拥塞控制的研究

拥塞控制是解决网络拥塞问题的重要策略。本文主要从流量预测和流量整形两个方面提出了降低拥塞率的通用方法．对传统的漏桶算法和RED算法做了改进。使得它们在降低拥塞率的同时又照顾到了公平性和效率。     

对网络QoS中拥塞控制的研究

拥塞控制是解决网络拥塞问题的重要策略。本文主要从流量预测和流量整形两个方面提出了降低拥塞率的通用方法,对传统的漏桶算法和RED算法做了改进,使得它们在降低拥塞率的同时又照顾到了公平性和效率。     

一种交通拥堵指数智能计算方法

交通拥堵与许多因素密切相关,具有强烈的随机性、偶然性,导致当前交通拥堵指数计算误差大,可靠性差等问题,为了提高交通拥堵指数计算精度,提出了基于支持向量机的交通拥堵指数智能计算方法。先分析当前交通拥堵指数智能计算的研究进展,找到引起交通拥堵指数计算效果的原因,然后采集一段时间的交通拥堵指数序列,引入支持向量机对交通拥堵指数序列进行建模与分析,找到交通拥堵指数变化规律,最后采用具体仿真实验与其它交通拥堵指数智能计算方法进行了对照实验。结果表明,这种方法降低了交通拥堵指数计算误差,为可以为交通管理者提供有价值的信息,有利于缓解交通拥堵问题,具有比较明显的优越性。     

交通事故的多维关联规则分析

城市机动车数量的增加已经导致城市交通事故的频繁发生,能否对已发生事故作出正确的分析将直接影响到能否对未来类似事故的成功避免。本文提出一种使用数据挖掘领域中的多维关联规则技术分析大量交通事故记录的方法,通过找出可能导致交通事故发生的频繁因素组合来发现某些事故发生的规律,并将这些规律作为现实中作出预防举措的依据。     

Long-term traffic flow prediction using multivariate SSA forecasting in SDN based networks

Accurate and real-time traffic flow forecasting plays an important role in optimizing traffic routing enabling adaptive and sophisticated applications on the network. Managing and routing enormous traffic flow with dynamic behavior is a highly challenging task. However, arriving at a precise model for traffic forecasting in a short interval of time is not trivial because of the dynamic nature of traffic flow. A novel multivariate time series framework is designed to analyze and forecast the dynamic traffic flow in SDN based networks. The proposed framework adapts the Multivariate Singular Spectrum Analysis (MSSA) forecasting model and incorporates the Randomized Singular Value Decomposition (RSVD) to improve the accuracy of flow prediction. Simulations are conducted to evaluate the effectiveness of the proposed MSSA method. The proposed method predicts the long-term traffic fluctuation from the observed traffic traces. The SDN controller is trained using the traffic traces and future traffic flows are forecasted. The performance evaluation of the proposed method predicts real-time traffic trends accurately with 2.2% MAPE, 9.44 MAE and 13.803 RMSE. The results show that the learning ability of MSSA helps to forecast future network traffic with low prediction errors.     

A review of: “Posture. Sitting,standing, chair design and exercise.” By D. ZACHARKOW. (Springfield,IL: Charles C. Thomas, 1988.) [Pp. xxi + 433.] US$49·75. ISBN 0-398-05418-5.

Drivers experiencing rush hour congestion were interviewed using cellular telephones to study stress and coping responses. Measures were taken of each driver's predisposition to stress (trait stress) as well as their reactions to the experience of either low or high traffic congestion (state stress). Two interviews were conducted during the trip when drivers experienced both low and high congestion conditions. Although state stress was greatest for all drivers experiencing the high congestion condition, a trait X situation interaction was obtained, indicating that stress levels were highest for high trait stress drivers experiencing the congested roadway. In terms of trait coping behaviours, participants indicated a preference for direct over indirect behaviours. A greater variety of direct and indirect behaviours were reported in high congestion. Reports of aggressive behaviours showed the greatest increase from low to high congestion. Comments on the use of cellular telephones in methodology are offered.     

分布式网络流量预测模型的精度研究

网络流量模型是网络性能评价、网络协议设计、网络规划等的基础。本文设计基于分布式网络测试的时间与相关流量模型的方法,并采用该流量模型预测网络流量。文章提出网络流量预测精度的数学定义,网络测试实验表明,我们的流量模型具有更高的精度,并适用实际运行的网络环境。     

Design of Regulatory Traffic Light Control Systems with Synchronized Timed Petri Nets

Fixing the phases is one of the common methods to control an urban traffic network. Once a road is filled with a high traffic flow approaching its capacity, the conventional traffic light controller is not able to handle this traffic congestion phenomenon well. In this paper, we propose a novel regulatory traffic light control system to handle such traffic congestion by using synchronized timed Petri nets (STPNs). Three kinds of intersections in an urban traffic network are defined and employed to demonstrate our new regulatory traffic light control system models. Finally, the liveness and reversibility of the proposed STPN models are proven through the reachability graph analysis method. To our knowledge, this is the first work that solves a traffic congestion problem with a regulatory traffic light control technique that is effective in preventing vehicles from entering traffic congestion zones.     

一种网络流量监控算法设计

网络流量监控是实现网络性能分析和监控的重要手段,文章阐述了采用排队论建立网络流量分析的基本模型,得出网络的流量预测方式和稳态拥塞率求解公式,结合常用的网络流量监控参数,合理的实现对网络流量的估算和监控过程。     

计算机网络拥塞控制算法改进算法研究

网络拥塞是由于网络业务流不可预测的流量突发现象造成的。文章从考虑网络业务流突发现象产生的特点出发,采用可用带宽测量技术和流量整形技术,提出了一种针对传统网络拥塞控制算法的改进算法（TCP2Shape）。     

基于无线传感器网络技术在交通信息采集系统的应用

交通拥挤、道路阻塞、交通事故的频繁发生,以及交通造成的环境污染日益成为制约国民经济发展的瓶颈.为了防止交通拥堵现象的进一步恶化,各国政府纷纷启动智能交通计划.针对智能交通系统的关键在于交通信息采集,开发成本低、可大量布设到各个路口的基于无线传感器网络的交通信息采集系统.主要介绍应用于智能交通时无线传感网络的构成,包括无线传感器网络的体系构架机器如何应用于交通信息采集领域,以及要开发原型样机的软硬件指标和通信协议、生命周期分析.     

基于时间特征的网络流量预测模型

本文设计一种基于时间特征的网络流量预测模型,并采用该流量模型预测网络流量。文章提出网络流量预测误差的数学定义,根据测试实验表明,我们的流量模型具有更高的可用性,并适用实际运行的网络环境。     

无线传感器网络中一种ARMA流量预测的拥塞控制算法

在无线传感器网络中,由于各节点的通信能力、计算能力、存储能力等都比较有限,使其需采取有别于传统网络的拥塞控制策略.本文针对传感网络特有的多对一、多跳通信方式经常导致网络拥塞的缺陷,提出一种基于流量预测的拥塞避免算法(SCATP).该算法通过ARMA模型分析流经各节点的当前流量,预测网络下一时刻的拥塞状况,并据此进行流量分配,从而实现拥塞控制的同时保证数据的可靠传输.仿真实验表明,SCATP算法在延迟、抖动率、吞吐量和能量有效性等方面能有效改善网络的服务质量.     

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series had not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic immune algorithm (SSVRCIA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average, back-propagation neural network, and seasonal Holt–Winters models. Therefore, the SSVRCIA model is a promising alternative for forecasting traffic flow.     

Predictive congestion control of ATM networks: multiple sources/single buffer scenario

This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency.We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.