检索结果-内蒙古大学图书馆

The contribution of connected vehicles to network traffic control: A hierarchical approach

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期139卷

作者： Moradi, Hossein Sasaninejad, Sara Wittevrongel, Sabine Walraevens, Joris Univ Ghent SMACS Res Grp Dept Telecommun & Informat Proc Ghent Belgium

Connected vehicles (CVs) can considerably contribute to traffic control in the near future by providing real-time information. Such information can be exploited to identify the current traffic condition. However, challenges resulting from a high volume of data and lack of perfect knowledge in mixed traffic scenarios (consisting of CVs and conventional vehicles) have led to relatively sparse research in network-wide CV-based traffic control. To deal with such challenges, this paper proposes a hierarchical framework integrating three layers: (i) an intersection controller at individual intersections located in the center of the network, (ii) a network controller to regulate traffic coming into the network, and (iii) a phase controller to optimize the sequence of intersections' signals. This approach permits the inclusion of information of (a limited number of) CVs to estimate and control queue lengths at individual intersections as well as the overall network load. Simulations carried out in VISSIM validate our approach in comparison with alternative control systems such as the application of (1) a traffic responsive control, (2) a traffic accumulation regulator, or (3) a network signals coordinator. Moreover, comparisons between the proposed framework and a central system equipped with the information of all vehicles (giving rise to the optimal operation with, however, costly computation) under the consideration of different penetration rates of CVs and network layouts confirm that this framework results in competitive performance indicators, which, owing to the hierarchical structure, can be efficiently employed in real-time applications.

关键词： network traffic control Connected vehicles Mixed traffic Hierarchical approach VISSIM

来源：评论

学校读者我要写书评

暂无评论

network traffic control for multi-homed end-hosts via SDN

引用

IET COMMUNICATIONS 2020年第19期14卷 3312-3323页

作者： Al-Najjar, Anees Khan, Furqan Hameed Portmann, Marius Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Queensland Sch ITEE Brisbane Qld 4072 Australia

Software-defined networking (SDN) is an emerging technology of efficiently controlling and managing computer networks, such as in data centres, wide-area networks, as well as in ubiquitous communication. In this study, the authors explore the idea of embedding the SDN components, represented by SDN controller and virtual switch, in end-hosts to improve network performance. In particular, the authors consider load balancing across multiple network interfaces on end-hosts with different link capacity scenarios. The authors have explored and implemented different SDN-based load-balancing approaches based on OpenFlow software switches, and have demonstrated the feasibility and the potential of this approach. The proposed system has been evaluated with MultiPath transmission control protocol (MPTCP). The proposed results demonstrated the potential of applying the SDN concepts on multi-homed devices resulting in an increase in achieved throughput of 55% compared to the legacy single network approach and 10% compared to the MPTCP.

关键词： telecommunication congestion control computer networks resource allocation transport protocols network interfaces software defined networking telecommunication traffic computer centres network traffic control multihomed end-hosts software-defined networking computer networks data centres wide-area networks ubiquitous communication SDN components virtual switch network performance multiple network interfaces different link capacity scenarios different SDN-based load-balancing approaches OpenFlow software switches MultiPath transmission control protocol SDN concepts multihomed devices legacy single network approach

来源：评论

学校读者我要写书评

暂无评论

ST-DeLTA: A Novel Spatial-Temporal Value network Aided Deep Learning Based Intelligent network traffic control System

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

引用

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2020年第4期5卷 568-580页

作者： Tang, Fengxiao Mao, Bomin Fadlullah, Zubair Md Liu, Jiajia Kato, Nei Tohoku Univ Grad Sch Informat Sci GSIS Sendai Miyagi 9808577 Japan Northwestern Polytech Univ Sch Cyberspace Secur Xian Peoples R China

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.

关键词： Deep learning Training Heuristic algorithms network topology Routing protocols control systems Feature extraction network traffic control packets forwarding deep learning convolutional neural network (CNN) deep belief network (DBN)

来源：评论

学校读者我要写书评

暂无评论

An End-to-End Load Balancer Based on Deep Learning for Vehicular network traffic control

引用

IEEE INTERNET OF THINGS JOURNAL 2019年第1期6卷 953-966页

作者： Li, Jinglin Luo, Guiyang Cheng, Nan Yuan, Quan Wu, Zhiheng Gao, Shang Liu, Zhihan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Waterloo Elect & Comp Engn Dept Waterloo ON N2L 3G1 Canada

The infrastructure to vehicle (I2V) communication boosts a large number of prevailing vehicular services, which can provide vehicles with external information, storage, and computing power located at both mobile edge server (MES) and remote cloud. However, vehicle distribution is imbalanced due to the spatial inhomogeneity and temporal dynamics. As a consequence, the communication load for MES is imbalanced and vehicles may suffer from poor I2V communications where the MES is overloaded. In this paper, we propose a novel proactively load balancing approach that enables efficient cooperation among MESs, which is referred to as end-to-end load balancer (E2LB). E2LB schedules the cached data among MESs based on the predicted road traffic situation. First, a convolutional neural network (CNN) is applied to efficiently learn the spatio-temporal correlation in order to predict the road traffic situation. Then, we formulate the load balancing problem as a nonlinear programming (NLP) problem and a novel framework based on CNN is adopted to approximate the NLP optimization. Finally, we connect the above neural networks into an end-to-end neural network to jointly optimize the performance, where the input is the historical traffic situation while the output is the balanced scheduling solution. E2LB can guarantee the real-time scheduling, since the calling of a well-trained neural network only requires a small number of simple operations. Experiments on the trajectories of taxis and buses in Beijing demonstrate the efficiency and effectiveness of E2LB.

关键词： Convolutional neural network (CNN) deep learning end-to-end load balance network traffic control

来源：评论

学校读者我要写书评

暂无评论

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent network traffic control Systems

引用

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2017年第4期19卷 2432-2455页

作者： Fadlullah, Zubair Md. Tang, Fengxiao Mao, Bomin Kato, Nei Akashi, Osamu Inoue, Takeru Mizutani, Kimihiro Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan NTT Corp Network Innovat Labs Kanagawa 2390847 Japan

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

关键词： Machine learning machine intelligence artificial neural network deep learning deep belief system network traffic control routing

来源：评论

学校读者我要写书评

暂无评论

network traffic control based on a mesoscopic dynamic flow model

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年 66卷 3-26页

作者： Di Gangi, Massimo Cantarella, Giulio E. Di Pace, Roberta Memoli, Silvio Univ Messina Dept Civil Engn I-98100 Messina Italy Univ Salerno Dept Civil Engn Salerno Italy

The paper focuses on network traffic control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： network traffic control Meta-heuristics Multi-criteria optimisation Mesoscopic flow modelling

来源：评论

学校读者我要写书评

暂无评论

network-wide critical routes identification and coordinated control based on automatic vehicle identification data

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 172卷

作者： Chen, Peng Xu, Jiaming Mei, Yu Wei, Lei Beihang Univ Sch Transportat Sci & Engn Minist Ind & Informat Technol Key Lab Autonomous Transportat Technol Special Veh Beijing 100191 Peoples R China Baidu Dept Intelligent Transportat Syst 10 Shangdi 10th St Beijing 100085 Peoples R China Tech Univ Dresden Chair Traff Proc Automat Hettnerstr 3 D-01069 Dresden Germany

Coordinated signal control is an effective way to improve traffic efficiency in urban road networks. This study presents a network-wide coordinated signal control method based on the identification of critical routes and the partitioning of coordinated intersection groups using automatic vehicle identification (AVI) data. A movement-level network representation model is first proposed to identify critical routes by analyzing the fully sampled paths of individual vehicles extracted from the network's AVI data. Then, the optimization of the entire road network is divided into multiple coordinated intersection groups and potential isolated intersections by minimizing total delay, accounting for the interactions between cycle length and delay at intersections along all critical routes. Based on the ring-and-barrier scheme, the signal control problem for each coordinated intersection group is formulated as a mixed integer linear programming (MILP) model aimed at maximizing the bandwidth allocated to critical route bands. Utilizing real AVI data collected from the network of Baoding City, China, the experimental results demonstrate that the proposed partitioning method outperforms static clustering methods that rely on traffic state, e.g., density, for control purposes, particularly in coordinated intersection groups. In comparison to state-of-the-art studies, the analyses indicate that the comprehensive framework, which ranges from critical routes identification and network partition to traffic signal control based on AVI data, significantly enhances network performance by reducing the queue length and average delay at intersections.

关键词： network traffic control Signal coordination Critical routes Automatic vehicle identification Optimization control

来源：评论

学校读者我要写书评

暂无评论

traffic signal plans to decongest street grids

引用

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2022年 162卷 195-208页

作者： Sadek, Bassel Godier, Jean Doig Cassidy, Michael J. Daganzo, Carlos F. Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA

Two new synchronization strategies are developed for signalized grids of two-directional streets. Both strategies are found to reduce congestion significantly more than do other approaches. One of the strategies is static and the other adaptive. Both use a common timing pattern for all signals on the grid but use a different offset for each. The static strategy serves the morning rush by providing perfect forward progression on all streets in the directions that point toward a reference intersection, one that is located near the center of gravity of all workplaces. For the evening rush, perfect progression is achieved for all travel directions that point away from the reference intersection. The adaptive strategy toggles between this forward synchronization mode and a second mode suited for congestion, but only in a pre-determined district surrounding the reference intersection. Toggling is based on the district's real-time traffic density. The paper shows how to switch quickly between the two synchronization modes without resorting to unacceptably short phases. It also shows that if the grid is formed by two intersecting sets of parallel streets, even if unevenly spaced, then every street can be perfectly synchronized in one of its directions. As a result, an inbound driver in the morning, or an outbound driver in the evening, is guaranteed to encounter synchronized signals over the full length of her trip. Although this is not possible for more irregular grids, the paper shows how to modify the two strategies for this case, so that they still perform well. The strategies were benchmarked with simulations against a fixed, zero-offset strategy for many scenarios, because zero-offsets are known to work well under congestion. In one important scenario representing a severely congested morning rush, both strategies were also benchmarked against a state-of-the-practice computer program. While the state-of-the-practice program reduced the zero-offset delay by a modest 7%

关键词： network traffic congestion Oversaturated network traffic network traffic control Signal coordination

来源：评论

学校读者我要写书评

暂无评论

Protection Schemes in HPON networks Based on the PWFBA Algorithm

引用

SENSORS 2022年第24期22卷 9885页

作者： Roka, Rastislav Fujdiak, Radek Holasova, Eva Kuchar, Karel Orgon, Milos Misurec, Jiri Slovak Univ Technol Bratislava Inst Multimedia Informat & Commun Technol Ilkovicova 3 Bratislava 81219 Slovakia Brno Univ Technol Dept Telecommun Fac Elect Engn & Commun Tech 12 Brno 61600 Czech Republic

In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions.

关键词： optic fiber telecommunication network infrastructure network traffic control HPON PWFBA channel allocation optical fiber networks scheduling algorithms systems simulation telecommunication services

来源：评论

学校读者我要写书评

暂无评论

Near-optimal responsive traffic engineering in software defined networks based on deep learning

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 172-180页

作者： Salman, Mohammed, I Bin Wang Wright State Univ Dayton OH 45435 USA Miami Univ Oxford OH 45056 USA Anbar Univ Ramadi Anbar Iraq

The routing problem for traffic engineering can be solved using different techniques. For example, the problem can be formulated as a linear program (LP) or a mixed-integer linear program (MILP) that requires solving a complex optimization problem. Thus, this approach typically cannot be used for solving a large problem in real time. Alternatively, heuristic algorithms may be devised that, though fast, do not guarantee an optimal decision. This work proposes a novel design of a system that employs a deep learning model trained on optimal decisions to solve the routing problem. The model learns to adapt to traffic dynamics by updating the traffic split ratios to distribute traffic to a few paths between a source and a destination instead of frequently computing a single path for a source and destination pair. This solves the problem of network disturbance and traffic disruption. Specifically, we train two deep learning models: DNN (MLP), which is fully connected layers of neurons, and DNN (LSTM) that consists of a few layers of LSTM neural network and a dense layer. The two models are evaluated in a TE simulator. The system offers two important features of a good traffic engineering system: producing close to optimal traffic engineering results and responding to traffic dynamics in real time. We perform simulations on two topologies, the ATT North America topology, and a 4x4 grid topology. The results show that our proposed system can learn from optimal decisions to attain a responsive traffic engineering system. (c) 2022 Elsevier B.V. All rights reserved.

关键词： traffic engineering network traffic control Software defined networking Oblivious routing Optimization Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：