ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, has signed a network equipment Global Framework Agreement (GFA) with Vodafone on spanning ZTE’s complete telecom...ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, has signed a network equipment Global Framework Agreement (GFA) with Vodafone on spanning ZTE’s complete telecoms infrastructure equipment portfolio.展开更多
Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffi...Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.展开更多
软件定义网络(Softeware Defined Network, SDN)是一种新型的网络体系架构,目前已成为下一代互联网研究的热点。为了解决SDN中的网络信息安全问题,文章对SDN中的控制平面、数据平面和应用平面进行分析,梳理并总结了SDN管理中的相关网络...软件定义网络(Softeware Defined Network, SDN)是一种新型的网络体系架构,目前已成为下一代互联网研究的热点。为了解决SDN中的网络信息安全问题,文章对SDN中的控制平面、数据平面和应用平面进行分析,梳理并总结了SDN管理中的相关网络安全问题。提出了一种基于SDN的网络安全框架及安全策略,有效弥补传统网络结构中的网络安全缺陷问题,增强SDN网络安全级别,并建立一种基于终端用户限定与管理的SDN的网络安全框架及其安全策略。展开更多
传统公交专用道动态控制方法无法同时保证公交优先和车道利用率的提升,为解决该问题,本文提出车联网支持下公交专用道复用的动态清空控制方法(Dynamic Clearance Bus Lane,DCBL),建立随网联公交行驶车速和网联社会车辆换道时间动态变化...传统公交专用道动态控制方法无法同时保证公交优先和车道利用率的提升,为解决该问题,本文提出车联网支持下公交专用道复用的动态清空控制方法(Dynamic Clearance Bus Lane,DCBL),建立随网联公交行驶车速和网联社会车辆换道时间动态变化的清空框模型,同时定义换道迫切系数,结合模糊控制理论,设计考虑驾驶员换道心理的换道概率输出算法,以模拟驾驶员实际换道过程,最后通过数值仿真以验证DCBL控制方法的有效性。仿真实验结果表明:DCBL控制方法将适用的交通密度范围扩大至0~71 pcu·km^(-1),比传统的BLIP(Bus Lane with Intermittent priority)、IBL(Intermittent Bus Lane)控制方法适用范围增加了9~21 pcu·km^(-1);在40~70 pcu·km^(-1)的中高交通密度区间,DCBL控制方法将社会车辆平均车速保持在45.86 km·h^(-1),比传统控制方法提高了17.9%~24.7%,将公交平均车速保持在33.68 km·h^(-1),对比公交期望车速仅降低了6.4%;DCBL控制方法在路段中高密度区间对公交车的行驶延误小于25 s,比传统控制方法提高路段通行能力8.0%~18.3%。展开更多
文摘ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, has signed a network equipment Global Framework Agreement (GFA) with Vodafone on spanning ZTE’s complete telecoms infrastructure equipment portfolio.
文摘Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.
文摘软件定义网络(Softeware Defined Network, SDN)是一种新型的网络体系架构,目前已成为下一代互联网研究的热点。为了解决SDN中的网络信息安全问题,文章对SDN中的控制平面、数据平面和应用平面进行分析,梳理并总结了SDN管理中的相关网络安全问题。提出了一种基于SDN的网络安全框架及安全策略,有效弥补传统网络结构中的网络安全缺陷问题,增强SDN网络安全级别,并建立一种基于终端用户限定与管理的SDN的网络安全框架及其安全策略。
文摘传统公交专用道动态控制方法无法同时保证公交优先和车道利用率的提升,为解决该问题,本文提出车联网支持下公交专用道复用的动态清空控制方法(Dynamic Clearance Bus Lane,DCBL),建立随网联公交行驶车速和网联社会车辆换道时间动态变化的清空框模型,同时定义换道迫切系数,结合模糊控制理论,设计考虑驾驶员换道心理的换道概率输出算法,以模拟驾驶员实际换道过程,最后通过数值仿真以验证DCBL控制方法的有效性。仿真实验结果表明:DCBL控制方法将适用的交通密度范围扩大至0~71 pcu·km^(-1),比传统的BLIP(Bus Lane with Intermittent priority)、IBL(Intermittent Bus Lane)控制方法适用范围增加了9~21 pcu·km^(-1);在40~70 pcu·km^(-1)的中高交通密度区间,DCBL控制方法将社会车辆平均车速保持在45.86 km·h^(-1),比传统控制方法提高了17.9%~24.7%,将公交平均车速保持在33.68 km·h^(-1),对比公交期望车速仅降低了6.4%;DCBL控制方法在路段中高密度区间对公交车的行驶延误小于25 s,比传统控制方法提高路段通行能力8.0%~18.3%。
