Network Traffic Flow Research Articles

Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting

AbstractTraffic prediction is an important part of intelligent transportation system. Recently, graph convolution network (GCN) is introduced for traffic flow forecasting and achieves good performance due to its superiority of representing the graph traffic road structure network. Moreover, the dynamic GCN is put forward to model the temporal property of the traffic flow. Although great progress has been made, most GCN based traffic flow forecasting methods utilize a single graph for convolution, which is considered not enough to reveal the inherent property of traffic graph as it is influenced by many factors, for example weather, season and traffic accidents etc. In this paper, an exotic graph transformer based dynamic multiple graph convolution networks (GTDMGCN) is conceived for traffic flow forecasting. Instead of the single graph, multiple graphs are constructed to modulate the complex traffic network by the proposed graph transformer network. Additionally, a temporal gate convolution is proposed to get the temporal property of traffic flow. The proposed GTDMGCN model is evaluated on four real traffic datasets of PEMS03, PEMS04, PEMS07, PEMS08, and there are average increments of 9.78%, 7.80%, 5.96% under MAE, RMSE, and MAPE metrics compared with the current results.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception

Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn their behavior. However, most of the existing IoT honeypots, even the high interaction ones, are easily detected by an attacker who can observe honeypot traffic due to lack of real network traffic originating from the honeypot. This implies that, to build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows.
 To achieve this goal, we propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator.We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices.Through an extensive experimental evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT traffic generation tool significantly outperforms state of the art sequence and packet generators in remaining indistinguishable from real traffic even to an adaptive attacker.

Ground-Air Traffic Congestion Propagation Model Based on Hierarchical Control Interdependent Network

A multilayer network approach to model and analyze air traffic networks is proposed. These networks are viewed as complex systems with interactions between airports, airspaces, procedures, and air traffic flows (ATFs). A topology-based airport-airspace network and a flight trajectory network are developed to represent critical physical and operational characteristics. A multilayer traffic flow network and an interrelated traffic congestion propagation network are also formulated to represent the ATF connection and congestion propagation dynamics, respectively. Furthermore, a set of analytical metrics, including those of airport surface (AS), terminal controlled airspace (TCA), and area-controlled airspace (ACA), is introduced and applied to a case study in central and south-eastern China. The empirical results show the existence of a fundamental diagram of the airport, terminal, and intersections of air routes. Moreover, the dynamics and underlying mechanisms of congestion propagation through the AS-TCA-ACA network are revealed and interpreted using the classical susceptible-infectious-removed model in a hierarchical network. Finally, a high propagation probability among adjacent terminals and a high recovery probability are identified at the network system level. This study provides analytical tools for comprehending the complex interactions among air traffic systems and identifies future developments and automation of layered coupled air traffic management systems.

Traffic flow prediction model based on gated graph convolution with attention

As a significant part, flow forecasting is crucial to road planning, traffic control, and planning. Aiming at the problem of insufficient dependence of traffic prediction methods, we proposes an Attention-based Gating mechanism Graph Convolution Network (AGGCN). Firstly, it constructures traffic flow data and adjacency matrix based on the graph data construction method. Secondly, it utilized the diffusion graph convolution method to model the traffic flow in the traffic road network. Finally, the attention mechanism to predict the traffic flow. The experiments were carried out on two highway datasets PeMS04 and PeMS08 in California, USA. Compared with the 7 commonly used algorithms in traffic flow prediction, the MAE, MAPE, and RMSE of the AGGCN model are better than other baseline experiments.

Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting

Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting

Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting

Traffic flow forecasting is a basic function of intelligent transportation systems, and the accuracy of prediction is of great significance for traffic management and urban planning. The main difficulty of traffic flow predictions is that there is complex underlying spatiotemporal dependence in traffic flow; thus, the existing spatiotemporal graph neural network (STGNN) models need to model both temporal dependence and spatial dependence. Graph neural networks (GNNs) are adopted to capture the spatial dependence in traffic flow, which can model the symmetric or asymmetric spatial relations between nodes in the traffic network. The transmission process of traffic features in GNNs is guided by the node-to-node relationship (e.g., adjacency or spatial distance) between nodes, ignoring the spatial dependence caused by local topological constraints in the road network. To further consider the influence of local topology on the spatial dependence of road networks, in this paper, we introduce Ollivier–Ricci curvature information between connected edges in the road network, which is based on optimal transport theory and makes comprehensive use of the neighborhood-to-neighborhood relationship to guide the transmission process of traffic features between nodes in STGNNs. Experiments on real-world traffic datasets show that the models with Ollivier–Ricci curvature information outperforms those based on only node-to-node relationships between nodes by ten percent on average in the RMSE metric. This study indicates that by utilizing complex topological features in road networks, spatial dependence can be captured more sufficiently, further improving the predictive ability of traffic forecasting models.

Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting

Traffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traffic flow has highly non-linear and complex patterns due to external factors such as time and space. Due to the high stochasticity and uncertainty of traffic flow, the difficulty of traffic flow prediction increases gradually with increasing time steps. The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, different methods are compared on the same time-granularity dataset, leaving the adaptability and robustness of these methods undervalidated. To address the above challenges, a new traffic forecasting method, named Attention-Based Gated Recurrent Graph Convolutional Network (AGRGCN) is presented for short-term traffic flow prediction. The method can extract spatialtemporal dependencies in traffic flow. In addition, an attention mechanism, which can adaptively capture traffic data relationships at different time steps, is introduced to alleviate the problem of faster deterioration of model prediction performance for longer time steps. Using a road network distance-based graph enables the method better to capture the topological information in traffic flow data. Experiments were conducted on two traffic datasets with different time granularity to predict traffic flow in highway and urban contexts. The experimental results show that our model has certain advantages.

Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting

Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to simultaneously capture the short-term and long-term temporal relations carried by the traffic flow data, and also suffer the over-smoothing problem. To overcome the problems, we propose a hierarchical traffic flow forecasting network by merging newly designed the long-term temporal Transformer network (LTT) and the spatio-temporal graph convolutional networks (STGC). Specifically, LTT aims to learn the long-term temporal relations among the traffic flow data, while the STGC module aims to capture the short-term temporal relations and spatial relations among the traffic flow data, respectively, via cascading between the one-dimensional convolution and the graph convolution. In addition, an attention fusion mechanism is proposed to combine the long-term with the short-term temporal relations as the input of the graph convolution layer in STGC, in order to mitigate the over-smoothing problem of GCN. Experimental results on three public traffic flow datasets prove the effectiveness and robustness of the proposed method.

Dynamic Spatial–Temporal Convolutional Networks for Traffic Flow Forecasting

Because of the highly nonlinear and dynamic spatial–temporal correlation of traffic flow, timely and accurate forecasting is very challenging. Existing methods usually use a static adjacency matrix to represent the spatial relationships between different road segments, even though the spatial relationships can change dynamically. In addition, many methods also ignore the dynamic time-dependent relationships between traffic flows. To this end, we propose a new network model to model the spatial–temporal correlation of traffic flow dynamics. Specifically, we design a dynamic graph construction method, which can generate dynamic graphs based on data to represent dynamic spatial relationships between road segments. Then, a dynamic graph convolutional network is proposed to extract dynamic spatial features. We further propose a multi-head temporal attention mechanism to learn the dynamic temporal dependencies between different times and then use temporal convolutional networks to extract the dynamic temporal features. The experimental results on real data show that the model proposed in this paper has a better prediction performance than existing models.

Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author’s method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found.

SAFE: EFFICIENT DDOS ATTACK DEFENSE WITH ELASTIC TRAFFIC FLOW INSPECTION IN SDN-BASED DATA CENTERS

In this paper, we propose an efficient distributed denial-of-Service (DDoS) Attack deFEnse solution, namely SAFE, which utilizes an elastic traffic flow inspection mechanism, for Software-Defined Networking (SDN) based data centers. In particular, we first examine a leaf-spine SDN-based data center network, which is highly vulnerable to volumetric DDoS attacks. Next, we develop a rank-based anomaly detection algorithm to recognize anomalies in the amount of incoming traffic. Then, for the traffic flow inspection, we introduce a component called DFI (Deep Flow Inspection) running an Open vSwitch (OvS) that can be dynamically initiated (as a virtual machine) on-demand to collect traffic flow statistics. By utilizing deep reinforcement learning-based traffic monitoring from our previous study, the DFIs can be protected from the flow-table overflow problem while providing more detailed traffic flow information. Afterward, a machine learning-based attack detector analyzes the gathered flow rule statistics to identify the attack, and appropriate policies are implemented if an attack is recognized. The experiment results show that the SAFE can effectively defend against volumetric DDoS attacks while assuring a reliable Quality-of-Service level for benign traffic flows in SDN-based data center networks. Specifically, for TCP SYN and UDP floods, the SAFE attack detection performance is improved by approximately 40% and 30%, respectively, compared to the existing SATA solution. Furthermore, the attack mitigation performance, the ratio of dropped malicious packets obtained by the SAFE is superior by approximately 48% (for TCP SYN flood) and 52% (for UDP flood) to the SATA.

Multi-view dynamic graph convolution neural network for traffic flow prediction

The rapid urbanization and continuous improvement of road traffic equipment result in massive daily production of traffic data. These data contain the long-term evolution of traffic flow and dynamic changes in the traffic road network. Due to the complex topology of the traffic road network, traffic flow prediction is challenging as it contains complex, multi-periodic patterns, and is often affected by sudden events. In this paper, we propose a Multi-View Dynamic Graph Convolution Network (MVDGCN) that captures different levels of spatial–temporal dependencies to predict traffic flow. Firstly, we use the coupling graph convolution network to learn the relationship matrix among stations dynamically, capturing the spatial dependencies at different levels in the traffic network. Secondly, we establish three encoder–decoders, representing hourly, daily, and weekly views, to extract the evolution law of traffic flow from three different time periods. Finally, we use the dynamic fusion module to merge the spatial–temporal dependencies extracted from the multi-view encoder–decoders. We conducted experiments on two real datasets, NYCTaxi and NYCBike, and found that our proposed MVDGCN model outperformed the best baseline, improving the RMSE, MAE, PCC, and MAPE by 12.9%, 6.2%, 0.8%, and 6.5% respectively on the NYCBike dataset and 9.2%, 4.2%, 4.6%, and 3.0% respectively on the NYCTaxi dataset. These results show that the proposed MVDGCN model performs better than state-of-the-art algorithms.

From Control to Chaos: The Dynamic SDN Control Plane

Software-defined networking (SDN) has revolutionized the way networks are designed and managed, with the control plane being a critical component. The SDN control plane is responsible for managing and orchestrating network traffic flows, but its traditional static approach can be limiting in dynamic and complex network environments. This abstract explores the concept of a dynamic SDN control plane, which can adapt and respond to changing network conditions, leading to better network performance and efficiency. The dynamic control plane involves the use of machine learning algorithms, artificial intelligence, and network analytics to constantly monitor and adjust network policies in real-time. The dynamic SDN control plane empowers networks to move from a state of control to chaos, enabling greater flexibility, agility, and resilience in network management. This abstract provides insights into the potential benefits of a dynamic SDN control plane and highlights the challenges and opportunities in implementing this approach.

Vulnerability detection in the network traffic flow of the RADIUS protocol based on the object-oriented model


 
 
 The RADIUS protocol was analyzed from the point of view of its functionality and security. The internal structure and the division into functions were shown. There were described the structure of the RADIUS network, the functions of the network access server or NAS, and the RADIUS server. The advantages of the centralized secure data processing technology based on the RADIUS protocol were shown. Mechanisms of secure processing of requests at the authentication and authorization stage were described. Sources and types of protocol vulnerabilities were studied and possible attack scenarios were identified. The relevance of creating models for evaluating the vulnerabilities of the RADIUS protocol was substantiated, and the methodology for building the model was chosen. An object-oriented model of the RADIUS protocol has been developed. A software application was developed and various attacks on the RADIUS protocol were tested. A number of potential vulnerabilities have been identified.
 
 

Explainable AI-Based DDOS Attack Identification Method for IoT Networks

The modern digitized world is mainly dependent on online services. The availability of online systems continues to be seriously challenged by distributed denial of service (DDoS) attacks. The challenge in mitigating attacks is not limited to identifying DDoS attacks when they happen, but also identifying the streams of attacks. However, existing attack detection methods cannot accurately and efficiently detect DDoS attacks. To this end, we propose an explainable artificial intelligence (XAI)-based novel method to identify DDoS attacks. This method detects abnormal behaviours of network traffic flows by analysing the traffic at the network layer. Moreover, it chooses the most influential features for each anomalous instance with influence weight and then sets a threshold value for each feature. Hence, this DDoS attack detection method defines security policies based on each feature threshold value for application-layer-based, volumetric-based, and transport control protocol (TCP) state-exhaustion-based features. Since the proposed method is based on layer three traffic, it can identify DDoS attacks on both Internet of Things (IoT) and traditional networks. Extensive experiments were performed on the University of Sannio, Benevento Instrution Detection System (USB-IDS) dataset, which consists of different types of DDoS attacks to test the performance of the proposed solution. The results of the comparison show that the proposed method provides greater detection accuracy and attack certainty than the state-of-the-art methods.

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting

The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone function, which are then input to the multi-graph neural network with the goal of extracting and aggregating features in order to realize the complex spatial dependence of each station’s passenger flow. Furthermore, based on periodic features of historical traffic flows, we categorize the flow data into three time patterns. Specifically, we propose two different self-attention mechanisms to fuse high-order spatiotemporal features of traffic flow. The final step is to integrate the two modules and obtain the output results using a gated convolution and a fully connected neural network. The experimental results show that the proposed model has better performance than eight other baseline models at 10 min, 15 min and 30 min time intervals.

Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.

Decentralized Optimization of Multiarea Interconnected Traffic-Power Systems With Wind Power Uncertainty

The high proliferation of electric vehicles has intensified the interdependency between traffic networks (TNs) and power distribution networks (PDNs). Accordingly, this article proposes a decentralized optimization framework for the multiarea optimal traffic-power flow (OTPF) problem, in which the models of PDNs and TNs are established separately in a distributed manner. A multistage distributionally robust optimization (MDRO) model is formulated for PDNs to address wind power uncertainty, with the introduction of a traffic assignment problem to describe the distribution of traffic flows in TNs. Furthermore, an improved alternative direction method of multipliers (I-ADMM) algorithm is developed to solve the multiarea OTPF problem. Numerical results from a three-area traffic-power coupled system demonstrate that the proposed MDRO model bears a cost $89.50 lower than that of the multistage robust optimization model, while the I-ADMM algorithm yields a solving time only one-third that of the traditional ADMM.