Network Traffic Flow Research Articles

Interplay of network topologies in aviation delay propagation: A complex network and machine learning analysis

In this study, the fundamental characteristics of flight delay propagation and the key factors influencing such propagation are investigated. Three distinct types of networks were constructed: an aviation network, a traffic flow network, and a delay propagation network. Employing complex network theory, an analysis of the fundamental topological attributes of each network was conducted, exploring the interrelations among these attributes. Findings reveal a notable resemblance between the network topology attributes of the delay propagation network and the traffic flow network. The delay propagation network overall adheres to scale-free network attributes, with a few dominant major airports playing a pivotal role in the delay propagation process. Furthermore, it was uncovered that smaller airports are more susceptible to the influence of delay propagation than their larger counterparts. Delays, in a general sense, exhibit a pronounced tendency to aggregate and spread extensively within the realm of smaller airports. Regarding delay propagation, airport traffic emerges as the primary factor precipitating this phenomenon, with a feature importance score reaching 0.889. An escalation in airport traffic significantly amplifies the extent of delay propagation, yet concurrently, this escalation is not incessant but stabilizes beyond a certain threshold.

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency

Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers’ real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler’s real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers’ travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system.

A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobility

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics.

Effect of road pricing on the spatial distribution of traffic flow in a grid network

This paper develops a continuous approximation model for analyzing the effect of road pricing on the spatial distribution of traffic flow. The traffic flow density, which describes traffic flow as a function of position, is derived for a rectangular city with a grid network. The analytical expression for the traffic flow density demonstrates how the toll level, the size and shape of the toll area, and the shape of the city affect the spatial distribution of traffic flow. As the size of the toll area increases, reducing the traffic flow density at the city center becomes difficult. As the aspect ratio of the toll area increases, the traffic flow density at the city center increases. The shape of the city has less impact on the traffic flow density than the shape of the toll area.

TELECOMMUNICATIONS ENERGY EFFICIENCY: OPTIMIZING NETWORK INFRASTRUCTURE FOR SUSTAINABILITY

The global telecommunications industry is facing significant challenges due to the rapid growth in data traffic and the growing environmental concerns associated with these networks. This paper explores strategies for optimizing network design and operation towards a greener, more sustainable future. Key challenges include the environmental impact of energy consumption, which accounts for 2-3% of global electricity consumption. The paper focuses on optimizing network design and operation, exploring energy-saving techniques and innovations, and revealing advanced network management optimizations. It also explores the transformative role of renewable energy sources like solar, wind, and geothermal power in mitigating the carbon footprint of telecommunications networks. The paper evaluates on-site renewable energy generation and power purchase agreements (PPAs) as integral components of a sustainable energy strategy. The paper highlights the potential of a holistic approach to telecommunications energy efficiency, including deploying energy-efficient hardware, using green data centers equipped with advanced power management techniques, implementing intelligent power management systems, and optimizing network traffic flow. Network management optimization involves regular evaluation and optimization of infrastructure, eliminating redundancy and underutilized equipment, and employing software-defined networking (SDN) and Network Function Virtualization (NFV). Renewable energy integration is also discussed, with on-site renewable energy generation and power purchase agreements emerging as powerful tools in reducing dependence on traditional grids and fossil fuels. The integration of AI, machine learning, and blockchain technology is also explored. By implementing these strategies, the telecommunications industry can contribute significantly to global environmental goals while securing enduring economic benefits. The implications of a sustainable telecommunications infrastructure extend beyond immediate energy savings, encompassing enhanced brand image, cost-effectiveness, and compliance with evolving regulations.
 Keywords: Telecommunications, Energy Efficiency, Network Design, Green Networks, Network Management, Emerging Technologies.

STGAFormer: Spatial–temporal Gated Attention Transformer based Graph Neural Network for traffic flow forecasting

Traffic flow prediction is a critical component of Intelligent Transportation Systems (ITS). However, the dynamic temporal variations in traffic flow, especially in potential occurrence of unexpected incidents, pose challenges to the prediction of traffic flow. This paper proposes a Spatial–temporal Gated Attention Transformer (STGAFormer) model based Graph Neural Network(GNN), leveraging the encoder architecture of the transformer. The gated temporal self-attention in the model, a novel module, can improve the model’s ability to make long-term predictions and handle sudden traffic incidents by enhancing the extraction of both local and global temporal features. Additionally, this paper proposes a distance spatial self-attention module to extract spatial features, which employs thresholding to selectively identify crucial features from both nearby and distant regions. In this way, the model’s ability to assimilate critical spatial information is promoted. Moreover, our model incorporates a diverse range of inputs, including traffic flow attributes, periodicity, proximity adjacency matrix, and adaptive adjacency matrix. Experiments from four real datasets demonstrate that STGAFormer achieves state-of-the-art performance, especially the MAE value of the PeMS08 dataset experiment is improved by 3.82%. This method offers valuable insights and robust support for future transportation planning.

Multi-Scale Dynamic Hypergraph Convolutional Network for Traffic Flow Forecasting

Multi-Scale Dynamic Hypergraph Convolutional Network for Traffic Flow Forecasting

Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: An empirical study in Ningbo, China.

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies.

Scalable Network Intrusion Detection in Cloud Environments through Parallelized Swarm-Optimized Neural Networks

Cloud computing (CC) offers on-demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remain a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffic flow that targets a cloud environment. Network Intrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce the problem. In this study, we gather an NSL-KDD network traffic dataset. Secondly, collected data is preprocessed using Z-Score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%).

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization

Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization

Traffic flow simulation study under large gathering activities based on VISSIM

Based on the above background, this paper starts from the theory of traffic flow and the macro-basic diagram, and simulates and researches the traffic congestion problems generated in the period of large-scale gathering activities during festivals. First of all, this paper summarizes the time correlation and spatial correlation of traffic flow, analyses the judgement method and rating index of traffic congestion, and completes the macroscopic basic map; after defining the simulation parameters of each road segment and signal intersection in the road network, it is implemented by using the VISSIM simulation software; finally, it carries out the simulation analysis with the example of the New Year's Eve firework display at the Chaotianmen Gate in Chongqing, and calculates and analyses the traffic flow state of the road network with output data, and then compares the output data with that of the road network. Finally, we use the output data to calculate and analyses the traffic flow status of the road network, compare it with the traffic flow data under the smooth period, judge the congestion characteristics, draw the macroscopic basic map of the road network under each period, simulate the changes of the macroscopic basic map, analyses the impact of traffic congestion on the traffic flow of the road network, calculate the degree of improvement of the traffic status of the network, and put forward the reasonable optimization scheme to provide a decision-making basis for the evacuation of the traffic under the festival and large-scale gathering activities.

Spatio-Temporal Heterogeneous Graph-Based Convolutional Networks for Traffic Flow Forecasting

Traffic flow forecasting plays a crucial role in the construction of intelligent transportation. The aims of this paper are to fully exploit the spatial correlation between nodes in a traffic network and to compensate for the inability of graph-based deep learning methods to model multiple relationship types, resulting in inadequate extraction of spatially correlated information about the traffic network. In this paper, we propose a deep spatio-temporal recurrent evolution network based on the graph convolution network (STREGCN) for heterogeneous graphs. Specifically, we transform the traffic network into a multi-relational heterogeneous graph to improve the information representation of the graph. This allows our model to capture multiple types of spatially relevant information. In the temporal dimension, we use one-dimensional causal convolution based on the gated linear unit to extract the temporal correlation information of the traffic flow. In addition, we designed the output of the spatio-temporal convolution module to obtain the final traffic flow predictions after a fully connected layer. Experiments on real datasets illustrate the effectiveness of the proposed STREGCN model and show the importance of representing information through heterogeneous graphs for the task of traffic flow prediction.

Spatio-temporal fusion graph convolutional network for traffic flow forecasting

In most recent research, the traffic forecasting task is typically formulated as a spatio-temporal graph modeling problem. For spatial correlation, they typically learn the shared pattern (i.e., the most salient pattern) of traffic series and measure the interdependence between traffic series based on a predefined graph. On the one hand, learning a specific traffic pattern for each node (traffic series) is crucial and essential for accurate spatial correlation learning. On the other hand, most predefined graphs cannot accurately represent the interdependence between traffic series because they are unchangeable while the prediction task changes. For temporal correlation, they usually concentrate on contiguous temporal correlation. Therefore, they are insufficient due to their lack of global temporal correlation learning. To overcome these aforementioned limitations, we propose a novel method named Spatio-Temporal Fusion Graph Convolutional Network (STFGCN). In the spatial aspect, we introduce a node-specific graph convolution operation to learn the node-specific patterns of each node (traffic series). Then, an adaptive adjacent matrix is introduced to represent the interdependence between traffic series. In the temporal aspect, a contiguous temporal correlation learning module is introduced to learn the contiguous temporal correlation of traffic series. Furthermore, a transformer-based global temporal correlation learning module is introduced to learn the global dependence of the traffic series. Experimental results show that our method significantly outperforms other competitive methods on two real-world traffic datasets (PeMSD4 and PeMSD8).

Adaptive graph generation based on generalized pagerank graph neural network for traffic flow forecasting

Adaptive graph generation based on generalized pagerank graph neural network for traffic flow forecasting

Empirical analysis of congestion spreading in Seoul traffic network.

Understanding how local traffic congestion spreads in urban traffic networks is fundamental to solving congestion problems in cities. In this work, by analyzing the high-resolution data of traffic velocity in Seoul, we empirically investigate the spreading patterns and cluster formation of traffic congestion in a real-world urban traffic network. To do this, we propose a congestion identification method suitable for various types of interacting traffic flows in urban traffic networks. Our method reveals that congestion spreading in Seoul may be characterized by a treelike structure during the morning rush hour but a more persistent loop structure during the evening rush hour. Our findings suggest that diffusion and stacking processes of local congestion play a major role in the formation of urban traffic congestion.

Dynamic spatial–temporal graph convolutional recurrent networks for traffic flow forecasting

Traffic flow forecasting is crucial for making appropriate route guidance and vehicle scheduling schemes in intelligent transportation systems. However, recent graph-based methods leverage pre-defined static graphs to describe the spatial–temporal characteristic of road networks. The interactions of two road segments are changeable under the influence of natural environmental and socioeconomic factors, while these methods are not sufficient to capture the complicated dynamic correlations of different nodes. To address these problems, we propose a novel dynamic graph-based deep learning framework with dynamic graph recurrent network for traffic flow forecasting, called Dynamic Spatial–temporal Graph Recurrent Neural Networks. In this framework, a novel dynamic graph generator is designed to obtain the dynamic representation of nodes, which employs multi-head attention network and dynamic node embedding to capture hidden spatial dependency more effectively. To infer the edge status of dynamic graph at different times, the generated dynamic graph is trained as special time series data via dynamic graph recurrent neural network for downstream time-series prediction. In contrast to methods straightforwardly concatenating static graphs and dynamic graphs, a novel fusion framework integrates two-channel convolutional networks with penalty terms and a gate fusion layer to extract dynamic spatial dependency from multiple graphs for improving forecasting accuracy and reducing computational consumption. Experiments on three real-world datasets are carried out to evaluate the superior performance of our model. Compared with previous state-of-the-art baselines, the proposed method performs much better with 10%–26% improvements on three datasets. The results also indicate that our model is robust against emergent traffic situations.

Global efficiency and network structure of urban traffic flows: A percolation-based empirical analysis.

Making the connection between the function and structure of networked systems is one of the fundamental issues in complex systems and network science. Urban traffic flows are related to various problems in cities and can be represented as a network of local traffic flows. To identify an empirical relation between the function and network structure of urban traffic flows, we construct a time-varying traffic flow network of a megacity, Seoul, and analyze its global efficiency with a percolation-based approach. Comparing the real-world traffic flow network with its corresponding null-model network having a randomized structure, we show that the real-world network is less efficient than its null-model network during rush hour, yet more efficient during non-rush hour. We observe that in the real-world network, links with the highest betweenness tend to have lower quality during rush hour compared to links with lower betweenness, but higher quality during non-rush hour. Since the top betweenness links tend to be the bridges that connect the network together, their congestion has a stronger impact on the network's global efficiency. Our results suggest that the spatial structure of traffic flow networks is important to understand their function.

Short-term traffic flow prediction based on weather factors analysis and neural network

With the progress of urban transportation infrastructure and the construction of intelligent transportation system, sufficiently accurate traffic flow prediction is of increasing importance for urban planning and management. This paper based on traffic flow data in a section of highway in Los Angeles, USA, we combine external qualitative data such as weather and work calendars, analyze and process the factors significantly influencing traffic flow first, then train and test the traffic flow data by building machine learning Support Vector Regression (SVR) model and the Back Propagation neural network (BP neural network). The test results show that the BP neural network traffic flow prediction model incorporating the external environment is more accurate, and that the external environment has a significant impact on the traffic flow prediction problem. If more significantly influencing factors can be taken into account, or if the current algorithm can be optimized, the prediction accuracy will have further improved, thus effectively enhancing the efficiency and safety of traffic systems.