Network Traffic Management Research Articles

Proactive content caching using surplus renewable energy: A win–win solution for both network service and energy providers

In recent years, the unprecedented uptake of streaming services has forced the telecommunication industry to rethink their traditional approach to delivering broadband services. Streaming services contribute to peak traffic load and slow down Internet speeds during the rush hours. Proactive content caching, which stores popular content in helper nodes at the network edge, can help to reduce the peak traffic load. While the focus of proactive caching so far is on network traffic management, the prospect of utilizing surplus renewable energy for content caching has not been adequately addressed. This research shows that proactive content caching using surplus renewable energy has the potential to reduce energy-related costs and bring significant benefits for both network and energy providers. Our research is based on the hypothesis that if proactive caching is performed when solar energy is available at daytime, energy related-costs can be significantly reduced. We formulate the research problem of finding the optimum time for proactive caching as an optimization problem, and minimize energy-related costs. Numerical analysis shows that the proposed approach not only provides significant economic benefits to service providers and households, but also reduces peak-to-average disparity of the energy load and the Internet traffic load.

Effect of reducing the price of anarchy on fairness in highway resource allocation for individual users

The price of anarchy (PoA) is the efficiency loss of a system resulting from individual route choice behavior. Network traffic management is often aimed at moving the system from the user equilibrium (UE) state to the system optimum (SO) state, or to reduce the PoA. This, however, may have a negative impact on individual users. This paper examines the effect of reducing the PoA on individual users or user groups. We quantify the gains and losses of users or user groups and the room for improvement resulting from a change from the UE state to the SO state with a generalized cost function comparable to the BPR function. Properties of changes in flows and costs for alternative routes and their relationships to one another are derived. The implication of traffic control aimed at reducing the price of anarchy and at the same time ensuring fairness in highway resource allocation is discussed.

Network Monitoring Tools and Techniques uses in the Network Traffic Management System

Network Monitoring Tools, Vendors and software's domain is huge, without a doubt. For server IT monitoring and in an ever changing marketplace new utilities, tools and software are being invented pretty much consistently. We have experienced the same number of devices as we could discover and gathered together the best ones in simple to peruse position and featured their fundamental qualities and why it think they are in the top class of instruments to use in IT framework and business.

An Efficient and Scene-Adaptive Algorithm for Vehicle Detection in Aerial Images Using an Improved YOLOv3 Framework

Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene-adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context-aware-based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length–width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.

Toward an integrated traffic law enforcement and network management in connected vehicle environment: Conceptual model and survey study of public acceptance

The increasing number of traffic accidents and their associated traffic congestion have prompted the development of innovative technologies to curb such problems. This paper proposes a novel score-based traffic law-enforcement and network management system (SLEM) that is based on connected vehicles (CV) technology. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws. The proposed system adopts a rewarding mechanism that rewards high-performing drivers and penalizes low-performing drivers who fail to obey the laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to achieve an optimal traffic pattern in the network in which high-score drivers experience less congestion and a higher level of safety. A nationwide survey study was conducted to measure public acceptance of the proposed system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models were developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65.7 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM.

Multivariate Multi-Order Markov Multi-Modal Prediction With Its Applications in Network Traffic Management

Predicting the future network traffic through big data analysis technologies has been one of the important preoccupations of network design and management. Combining Markov chains with tensors to implement predictions has received considerable attention in the era of big data. However, when dealing with multi-order Markov models, the existing approaches including the combination of states and Z-eigen decomposition still face some shortcomings. Therefore, this paper focuses on proposing a novel multivariate multi-order Markov transition to realize multi-modal accurate predictions. First, we put forward two new tensor operations including tensor join and unified product (UP). Then a general multivariate multi-order (2M) Markov model with its UP-based state transition is proposed. Afterwards, we develop a multi-step transition tensor for 2M Markov models to implement the multi-step state transition. Furthermore, an UP-based power method is proposed to calculate the stationary joint probability distribution tensor (i.e., stationary joint eigentensor, SJE) and realize SJE based multi-modal accurate predictions. Finally, a series of experiments under various Markov models on real-world network traffic datasets are conducted. Experimental results demonstrate that the proposed SJE based approach can improve the prediction accuracy for network traffic by highest up to 38.47 percentage points compared with the Z-eigen based approach.

Adaptive Traffic Management in Heterogeneous Wireless Networks

The adoption of wireless technologies is due to user requirement, small size of devices with multiple networking interfaces. The current issues which are perform major role of mobility, reassembly, routing, and security. Consistent mobility is one of the main challenges in the existing and future heterogeneous wireless networks. Consistent mobility in heterogeneous wireless network needs integration of different network technologies. Most of currently available communication and computing systems uses more system time for completing the job. Processing power and time limit for processing a task/query, a system is required that offers device mobility (BS mobility) and computational mobility as per the requirement of the applications. We have evaluated the MAC-SF for network selection, traffic management, load balancing, and network delay.

‘What Drives Commuter Behaviour?’: A Bayesian Clustering Approach for Understanding Opposing Behaviours in Social Surveys

SummaryThe city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large on-line survey, and similar individuals have been grouped to explore distinct behaviour profiles to inform intervention design to reduce commuter congestion. Statistical analysis within societal applications benefit from incorporating available social scientist expert knowledge. Current clustering approaches for the analysis of social surveys assume that the number of groups and the within-group narratives are unknown a priori. Here, however, informed by valuable expert knowledge, we develop a novel Bayesian approach for creating a clear opposing transport mode group narrative within survey respondents, simplifying communication with project partners and the general public. Our methodology establishes groups characterizing opposing behaviours based on a key multinomial survey question by constraining parts of our prior judgement within a Bayesian finite mixture model. Drivers of group membership and within-group behavioural differences are modelled hierarchically by using further information from the survey. In applying the methodology we demonstrate how it can be used to understand the key drivers of opposing behaviours in any wider application.

A Fuzzy TOPSIS with Affinity Weight for Big Data Projects: Managing Cloud Solution Problems

This study underlines a fuzzy decision making method for determining weights of criteria that constitute for solving cloud solution problems in managing big data projects. The weight determination of cloud for big data projects is crucial since many uncertain and vagueness criteria that need to be considered concurrently. Furthermore, these criteria involve network performance, schedule and traffic management of cloud solutions problem. In response to these challenges, the affinity set applies to Fuzzy TOPSIS (FTOPSIS) method to propose timedependent weights of three criteria for managing big data projects. A major advantage of the affinity weights is that it incorporates performance-traffic management relationships between all criteria. This affinity weight with FTOPSIS method helps to solve the cloud solution problems. This paper also includes the same examples with different methods to compare and validate the proposed method. The proposed seven-step of affinity weight with FTOPSIS method finally managed to solve the cloud solution problem and the result was beautifully consistent with the other two methods.

<b>Recent Trends and Topics in Research and Development Related to Transportation and Traffic Planning Technology</b>

The signalling and transport information division of RTRI aims to contribute to enhancing safety, reliability, and convenience through research and development in signalling systems, communication network technology, transportation planning and traffic management, and condition monitoring technology for railway facilities. This paper outlines the recent trends in research and development aimed at new train operation and control technology for safer and more stable transportation services. This paper further reports on recent development in technology for detecting obstacles on the track, to reduce railway traffic accidents.

Centralised and decentralised signal timing optimisation approaches for network traffic control

This study develops and analyses the centralised and decentralised solution procedures for urban network traffic management through an optimal control framework. The optimal control is formulated based upon the Hamilton-Jacobi formulation of kinematic wave model. Following the problem decomposition under the decentralised scheme, we introduce the use of semi-analytical performance derivatives when developing the decentralised solution algorithm. The proposed control strategies are applied to a set of test scenarios constructed from a real road network in Central London in the UK. Specific interests in the investigation include comparison of the performance gain and computational effort of the two strategies under different circumstances. We also investigate effect of network decomposition strategies on the performance of the solution algorithm. This study generates insight on urban traffic management with use of traffic flow theory, decentralised optimisation, and network decomposition techniques.

Intelligent Traffic Management Service Using Fuzzy Logic Controller in High Speed Networks

Network traffic management is a core area of research that is of great importance in the field of communication. This paper proposes a new scheme for controlling router side traffic in networks by updating source sending rate according to its IQ size. A new fuzzy controller is to be modelled to implement the proposed system. Simulation results and comparisons has verified the effectiveness and showed that our proposed scheme can achieve better performances than the existing protocols.

Traffic Paradox Under Different Equilibrium Conditions Considering Elastic Demand

Traffic paradox is an important phenomenon which needs attention in transportation network design and traffic management. Previous studies on traffic paradox always examined user equilibrium (UE) or stochastic user equilibrium (SUE) conditions with a fixed traffic demand (FD) and set the travel costs of links as constants under the SUE condition. However, traffic demand is elastic, especially when there are new links added to the network that may induce new traffic demand, and the travel costs of links actually depend on the traffic flows on them. This paper comprehensively investigates the traffic paradox under different equilibrium conditions including the user equilibrium and the stochastic user equilibrium with a fixed and elastic traffic demand. Origin-destination (OD) mean unit travel cost (MUTC) has been chosen as the main index to characterize whether the traffic paradox occurs. The impacts of travelers’ perception errors and travel cost sensitivity on the occurrence of the traffic paradox are also analyzed. The conclusions show that the occurrence of the traffic paradox depends on the traffic demand and equilibrium conditions; higher perception errors of travelers may lead to a better network performance, and a higher travel cost sensitivity will create a reversed traffic paradox. Finally, several appropriate traffic management measures are proposed to avoid the traffic paradox and improve the network performance.

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">software-defined cloud computing</i> (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

Generating traffic flow and speed regional model data using internet GPS vehicle records

Nowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in filter speed and accelerations, assign buffers to the road network, aggregate speed by street, fill missing number of lanes, generate traffic flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling.•124 million GPS observations from electronic devices were used to generate traffic flow.•Spatial bias was investigated and accounted for using independent local traffic count data.•Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.

A Decision Support System for Proactive-Robust Traffic Network Management

Real-time traffic network management systems are envisioned to provide network operators with decision support capabilities to alleviate recurrent and non-recurrent congestion. These capabilities involve predicting the network congestion dynamics and facilitating the development of proactive traffic management schemes that integrate traffic control and demand management strategies. However, traffic networks are subject to numerous sources of stochasticity that make it difficult to accurately predict their operational conditions and generate effective traffic management schemes to cope with these conditions. This paper presents a decision support system for proactive-robust traffic network management, which accounts for uncertainty in the network operational conditions. The objective is to develop robust traffic management schemes such that the network overall performance remains close to optimality under all possible future operational conditions. The modeling framework of the system is presented, which adopts a rolling horizon framework that integrates a meta-heuristic search algorithm and a dynamic traffic assignment simulation-based methodology. The system performance is examined through application to the traffic network of the US-75 corridor in Dallas, TX, USA.

Centralised and decentralised signal timing optimisation approaches for network traffic control

Abstract This study develops and analyses the centralised and decentralised solution procedures for urban network traffic management through an optimal control framework. The optimal control is formulated based upon the Hamilton-Jacobi formulation of kinematic wave model. Following the problem decomposition under the decentralised scheme, we introduce the use of semi-analytical performance derivatives when developing the decentralised solution algorithm. The proposed control strategies are applied to a set of test scenarios constructed from a real road network in Central London in the UK. Specific interests in the investigation include comparison of the performance gain and computational effort of the two strategies under different circumstances. We also investigate effect of network decomposition strategies on the performance of the solution algorithm. This study generates insight on urban traffic management with use of traffic flow theory, decentralised optimisation, and network decomposition techniques.

Mobile app identification for encrypted network flows by traffic correlation

Mobile application (simply “app”) identification at a per-flow granularity is vital for traffic engineering, network management, and security practices. However, uncertainty is caused by a growing ...

Characterizing Critical Transition State for Network Fundamental Diagram

Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level. The network traffic flow state analysis is a fundamental problem for the MFD-based applications. Theoretical and experimental investigations have provided insights into the dynamics and characters of traffic flow states. Although many empirical studies had been conducted in the field of MFD, few studies were dedicated to investigate the network traffic flow states with field data. This study aims to develop a data-driven method based on time series analysis of MFD state points to characterize critical transition state (CTS) of network traffic flow using field data. The proposed method was tested in a real network of Kunshan City, China. The test results showed that the CTS points can be well captured by the proposed method. The identified CTS points distinguished the traffic states between free-flow state and optimal accumulation state, and the optimal accumulation state was characterized. The day-to-day pattern of CTS points was investigated by the Gaussian Mixture Model-based clustering model. An extended application of real-time identification of CTS points was also discussed. The proposed method is helpful to understand the temporal evolution process of network traffic flow and provides potentials for developing more reliable network traffic flow management strategies, such as optimizing traffic signal plans and developing strategies for congestion tooling.

SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite.