Network Traffic Flow Research Articles

The Propagation of Congestion on Transportation Networks Analyzed by the Percolation Process

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

TrafficPro: A road speed prediction framework for urban signal-controlled road networks

In view of the problem that traditional deep learning models do not consider the active time-varying characteristics of traffic flow (signal control information) when predicting the speed of urban road networks, and have low prediction accuracy, this paper proposes a speed prediction framework based on generative adversarial networks and graph neural networks. In this framework, the generator network simultaneously encodes the road network traffic flow and signal control information through active and passive prediction modules to generate prediction results, and then uses the discriminator network to improve the generalization of the prediction results. This framework can obtain higher prediction accuracy than traditional time series models and deep learning models. In the real road network speed prediction scenario, this framework can reduce the prediction error compared to the best benchmark model 3%-4%.

Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

Digital traffic state analysis for urban regions considering complex multi-directional flow changes

In this research, a digital analytical model based on three-flow full-cycle state macroscopic fundamental diagram (MFD) with observable different perimeter control management and flow loading modes has been proposed on the basis of real network and peak hour traffic flow loading scenarios in Rostov-on-Don. It is known that the aim of digital management of urban intelligent traffic is to ultimately optimize the overall state of multi-area traffic operation in the city. The goal of this research was to provide a strong basis to establish digital management strategies for intelligent transport, determine the change trends of steady-state equilibrium of regional traffic flow and their effects on full-cycle states of traffic flow under various peripheral control management strategies and traffic loading patterns. Simulation results confirmed that with the evolution of traffic state, stricter peripheral control management always results in a wider attraction area ultimately giving rise to steady-state equilibrium of traffic flow.

Dynamic Multi-Scale Spatial-Temporal Graph Convolutional Network for Traffic Flow Prediction

This paper proposes a dynamic multi-scale spatial-temporal graph convolutional network (DS-STGCN) for traffic flow prediction. The network aims to comprehensively extract global and local dependencies in dynamic spatial-temporal data by inputting traffic network flow data to construct node feature graphs, topology graphs, and time slot feature graphs, capturing the complexity and dynamics of traffic flow. DS-STGCN interprets feature information of the traffic network from both spatial and temporal dimensions through dynamic multi-scale graph convolutional blocks. In the spatial dimension, these blocks use constraints at different levels to balance fine-grained local features and extensive global features, revealing the intrinsic structure of traffic flow data. In the temporal dimension, these blocks jointly learn with temporal convolutional blocks to capture multi-frequency time patterns and handle long sequence data, effectively extracting potential dependencies of time series. Furthermore, DS-STGCN effectively models the changing spatial-temporal relationships in road network flow by constructing dynamically adaptive updated adjacency tensors, generating dynamic graph structures to address the challenge of changing spatial-temporal relationships in the transportation system. Experimental results show that our method significantly outperforms other competing methods on five real traffic datasets (PEMS03, PEMS04, PEMS07, PEMS08 and METR-LA).

Spatiotemporal interactive dynamic adaptive adversarial graph convolution network for traffic flow forecasting

Accurate traffic flow forecasting is important for intelligent traffic management and control. To address the inability of existing methods to simultaneously capture the spatiotemporal dependence of traffic flows and the significant trend differences between predicted and real values, a traffic flow forecasting method based on a Spatiotemporal Interactive Dynamic Adaptive Convolutional Network (STIDAAG) is proposed. First, an interactive learning structure is designed to dynamically aggregate the spatiotemporal characteristics of the hidden nodes in the traffic network. Second, a dynamic adaptive graph generation network is designed based on the current and historical state to further capture the dynamic spatiotemporal characteristics. Finally, the adversarial graph convolutional network is used to optimize the loss for adversarial training to reduce the trend difference between the predicted and true values. The results of the experiment on four publicly available datasets indicate that STIDAAG outperforms both typical and advanced methods in terms of predictive performance.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Network traffic prediction based on PSO-LightGBM-TM

Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

Effect of chemical reaction on MHD Casson natural convection flow over an oscillating plate in porous media using Caputo fractional derivative

Fractional calculus is a branch of mathematics that extends the traditional definitions of calculus to include non-integer exponents. It has been successfully used to model a variety of physical processes, including the coiling of polymers, viscoelasticity, traffic flow, diffusive transport, fluid dynamics, electromagnetic theory, and electrical networks. However, fractional calculus has not yet been widely used to study the physical properties of non-Newtonian fluids flowing over accelerating porous plates. The present research examines the unsteady Casson flow over an infinitely horizontal porous plate, MHD and porosity impacts on fluid velocity are also discussed. The major goal here is to develop analytical outcomes for the tran-sient incompressible MHD Casson flow over an accelerating porous plate. This plate is oscillating in the x-direction and infinitely large in the y-direction and fluids flowing over it at y > 0 due to oscillation. Choosing the right choice of dimensionless variables allows the model's governing equations to become dimensionless. These dimensionless differential equations of the Casson fluid are calculated by applying the Laplace transform method. On velocity, the impacts of different factors are investigated. They are time, porosity parameter, oscillation frequency of plate, magnetic parameter, and Casson fluid parameter. As we rise the Casson flow parameter values, magnetic parameter, and Prandtl number we observe that the velocity drops. In contrast to other factors, the effects of Grashof number, oscillation frequency, fractional parameter, and porosity on the velocity profile are reversed. Using Mathcad software, graphs are sketched for various values of these parameters and are thoroughly described. The fluid properties discovered signif-icant findings and disclosed several characteristics for a range of flow parameters and fractional parameter values. Increasing the values of fractional parameters is shown to improve the characteristics of fluids, and this is beneficial in fitting experimental data related to heating and cooling processes, particularly in electronic equipment.

Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Using a Pre-Trained Language Model.

Most current methods use spatial-temporal graph neural networks (STGNNs) to analyze complex spatial-temporal information from traffic data collected from hundreds of sensors. STGNNs combine graph neural networks (GNNs) and sequence models to create hybrid structures that allow for the two networks to collaborate. However, this collaboration has made the model increasingly complex. This study proposes a framework that relies solely on original Transformer architecture and carefully designs embeddings to efficiently extract spatial-temporal dependencies in traffic flow. Additionally, we used pre-trained language models to enhance forecasting performance. We compared our new framework with current state-of-the-art STGNNs and Transformer-based models using four real-world traffic datasets: PEMS04, PEMS08, METR-LA, and PEMS-BAY. The experimental results demonstrate that our framework outperforms the other models in most metrics.

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Multi-step trend aware graph neural network for traffic flow forecasting

Traffic flow prediction plays an important role in smart cities. Although many neural network models already existed that can predict traffic flow, in the face of complex spatio-temporal data, these models still have some shortcomings. Firstly, they although take into account local spatio-temporal relations, ignore global information, leading to inability to capture global trend. Secondly, most models although construct spatio-temporal graphs for convolution, ignore the dynamic characteristics of spatio-temporal graphs, leading to the inability to capture local fluctuation. Finally, the current popular models need to take a lot of training time to obtain better prediction results, resulting in higher computing cost. To this end, we propose a new model: Multi-Step Trend Aware Graph Neural Network (MSTAGNN), which considers the influence of global spatio-temporal information and captures the dynamic characteristics of spatio-temporal graph. It can not only accurately capture local fluctuation, but also extract global trend and dramatically reduce computing cost. The experimental results showed that our proposed model achieved optimal results compared to baseline. Among them, mean absolute error (MAE) was reduced by 6.25% and the total training time was reduced by 79% on the PEMSD8 dataset. The source codes are available at: https://github.com/Vitalitypi/MSTAGNN.

Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope

Graph Convolutional Networks (GCNs), which can model data in non-Euclidean space, have received extensive attention in multivariate traffic flow forecasting in recent years. However, most of the existing GCN forecasting methods rely on adjacency matrices defined by road network knowledge, which cannot accurately capture the inherent spatial-temporal dependencies of complex multivariate traffic flows. In this paper, a multiscale information enhanced spatial-temporal GCN is proposed, which utilizes an adaptive graph learning layer to automatically extract the dependencies between variables and constructs adaptive adjacency matrices for different datasets. In addition, an information enhancement module is introduced to magnify perceptual scope of the model and strengthen the connection between information, so that the spatial-temporal graph convolution module can better capture the spatial-temporal dependencies in the traffic flow. We conducted experiments on multivariate traffic flow data from three different real-world scenarios to evaluate the effectiveness of the proposed method. Comparative experimental results consistently show that the proposed method outperforms existing GCNs, achieving an average improvement of 21.16% on Mean Absolute Error (MAE), 18.87% on Root Mean Square Error (RMSE), and 8.27% on R2 score (R2) in the three datasets, respectively, and the results demonstrate its excellent forecasting performance in the field of multivariate traffic flow forecasting.

Tolling scheme for traffic flow networks: modelling and control of positive systems

This article addresses modelling and control of traffic flow networks for congestion mitigation. We first model the traffic flow of an urban area and derive a state-space model composed of multiple compartments. Each compartment expresses the dynamics of the traffic demand that depends on drivers' decision-making on route choices influenced by tolling. Two practical properties are found in the proposed state space model: one is the positivity of the model, and the other is the conservation law in the number of vehicles. Based on the positivity of the model, we next propose the optimal tolling design aiming at the peak demand mitigation, which is expressed by nonlinear programming. We transform the nonlinear programming into a linear one, which is numerically tractable. Finally, we show that the proposed tolling design mitigates the peak demand in designated compartments in a numerical experiment and present a demonstration of the proposed control system.

Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver’s intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management.

A road network traffic flow data imputation method based on the fusion of spatiotemporal features and adversarial networks

In response to the problem of missing traffic flow data on highways, to solve the problem of insufficient mining of traffic flow characteristics using existing spatiotemporal correlation repair methods, a missing data repair method based on spatiotemporal fusion adversarial network is proposed based on analyzing the spatiotemporal characteristics of traffic flow, this method utilizes the fusion of GRU and GCN to capture the fine-grained spatiotemporal relationships of traffic flow, optimizes the generator and discriminator of the adversarial network, and achieves accurate repair of missing data. Experiments based on real data have shown that under various missing modes and rates, the model can learn the topological relationships of the road network, capture the temporal regularity and spatiotemporal correlation in the data, and effectively impute missing data.

Multi-attention gated temporal graph convolution neural Network for traffic flow forecasting

Multi-attention gated temporal graph convolution neural Network for traffic flow forecasting

Traffic State Comparison of Signalized Intersections Using SIDRA and SYNCHRO Software

The number of shopping malls and vehicles in Baghdad has quickly increased along with the city's population. This has resulted in an increase in daily trips, which affects the roadway network's traffic flow and causes congestion, particularly on Al-Mansour Street and Al-Rowad Street near the city center. To determine the best signal time, the 14th Ramadan signalized intersection was evaluated and analyzed using the SIDRA 8.0 and SYNCHRO 11.0 software. First, the findings revealed that the chosen traffic facility is presently experiencing a substantial reduction in service level that is leading to forced conditions (LOS "F"). It is suggested that the cycle time at the intersection of the 14th Ramadan be changed from 240 seconds to 160 seconds and 125 seconds in SIDRA and SYNCHRO, respectively. Signal timings have reduced the delay for the 14th Ramadan intersection (76.8%), thus improving LOS from (F) to (D) for the intersection. The best delays after optimization in SYNCHRO 11.0, however, were 31% reduction with LOS of (E and D). Cycle times have also been lowered for the intersection to 25% as part of the optimum signal timing optimization. In SYNCHRO 11.0, which is better than SIDRA 8.0, the back of the queue has improved at the 14th Ramadan intersection by 33%. However, SYNCHRO has a better representation than SIDRA for current traffic saturation

Optimizing gate control coordination signal for urban traffic network boundaries using multi-agent deep reinforcement learning

The macro-aggregated dynamic characteristics of urban network traffic flow encapsulated embodied in macroscopic fundamental diagram theory provide a concise perspective for network-level traffic control. However, accurately translating inter-regional macro-traffic flow control into fine-grained control at each intersection along the boundary remains challenging. To address this issue, a regional boundary intersections collaborative control method is proposed, leveraging the model-free data-driven advantages of multi-agent deep reinforcement learning. First, the proposed model develops a real-time communication module using a bi-directional long short-term memory model and an attention mechanism, enabling intersections to capture spatiotemporal features of the boundary traffic flow. Second, the feedback reward mechanism of the boundary intersection agent is meticulously constructed by considering the traffic state of the protected region, the boundary control pressure, and the traffic pressure of the intersection. The proposed model fosters cooperation at boundary intersections and delicately balances their macro-micro control contradictions based on mastering the boundary state information of the road network. Data-driven boundary gating control can fine-regulate the traffic flow distribution of the network from the perspective of macro-micro to promote its operation efficiency. The simulation experiments verify the effectiveness of the proposed multi-agent signal collaborative control method.