Traffic flow analysis largely depends on accurate predictions of microscopic speed. Due to the complexity and stochastic of real-world driving environments, traditional model-driven methods face significant challenges. In recent years, data-driven methods that combine advanced intelligent algorithms have emerged to address the issue of vehicle speed prediction. However, most existing studies primarily focus on the static spatiotemporal relationships between vehicles, with only a few exploring dynamic spatiotemporal correlations. In this study, a new graph structure, named the vehicle generalized-dynamic graph, is constructed to characterize the vehicle trajectory, emphasizing the spatial and temporal evolution of vehicles. In the spatial dimension, we employ the potential field theory to compute the field strength of vehicles in different motion states, enhancing the description of interdependencies among vehicles. In the temporal dimension, the movements of each vehicle at different time points are interrelated. Furthermore, we propose a novel neural network architecture, named the dynamic edge graph convolutional network (DEGCN), to address the vehicle speed prediction problem. The DEGCN model is evaluated through experiments on three real-world vehicle trajectory datasets. The experimental results demonstrate that the proposed model outperforms other baseline models in terms of prediction performance. Additionally, various ablation studies are conducted to evaluate the effectiveness of different components, the potential field theory, and the vehicle generalized-dynamic graph structure.

Efficient identification of key nodes is crucial to optimizing detector deployment and enhancing traffic monitoring in intelligent transportation systems. However, existing approaches often struggle to adapt to dynamic traffic variations, leading to suboptimal coverage and increased deployment costs. We propose bidirectional temporal convolutional graph attention networks (BTC-GATs) to address these limitations. This novel framework integrates bidirectional attention mechanisms to capture upstream and downstream dependencies, temporal convolutional networks for multiscale feature extraction, and graph attention networks for spatial information aggregation. BTC-GATs incorporates adaptive temporal modeling to capture nonlinear traffic dynamics, gradient-based variation analysis to quantify node influence, and a ranking mechanism that fuses attention coefficients with topological attributes to further enhance robustness and interoperability. In addition, a key node coverage study is conducted to examine the trade-off between accuracy and deployment efficiency. Extensive experiments on the California Highway PeMS04 dataset demonstrate that BTC-GATs outperforms benchmark methods in key node identification, offering superior accuracy and stability. Further analysis confirms its robustness under varying traffic conditions and initialization settings, highlighting its potential as a scalable, adaptive, and cost-effective solution for intelligent traffic monitoring. By facilitating efficient sensor placement, BTC-GATs contributes to improved data collection and congestion management in large-scale transportation networks.

This article provides a comprehensive analysis of the effects of connected and automated vehicles (CAVs) market penetration rate, spatial distribution, and V2X communication on the stability of heterogeneous traffic flow. We explore stability variations across different scenarios by developing a car-following model and analyzing stability conditions. Our findings show that increasing CAV market penetration enhances traffic flow stability by expanding the stable speed range and reducing the unstable speed range. Additionally, we investigate the influence of CAV spatial distribution and V2X communication on traffic flow stability. The results reveal that forming CAV platoons reduces intervehicle interference, thus improving stability. Furthermore, V2X communication enhances traffic flow stability by enabling more accurate prediction and control of car-following behaviors. We also examine the impact of cyberattacks on heterogeneous traffic flow, finding that such attacks cause unnecessary accelerations, decelerations, and delays, thereby increasing accident risks. Finally, we acknowledge the study’s limitations and propose future research directions, including nonlinear stability analysis of heterogeneous traffic flow and developing effective control strategies to mitigate the adverse effects of cyberattacks.

High-quality traffic flow data is foundational to the study of traffic issues and practical engineering applications. The development of traffic flow detection methods has greatly facilitated the collection of traffic data; however, anomalies caused by factors such as equipment, networks, and environmental conditions present a common challenge. Traditional data restoration methods, which require extensive complete historical data as prior knowledge, are difficult to implement. In this study, we first analyze and categorize the causes of anomalous data, and introduce strategies for anomaly identification in multi-source freeway data. Based on the unsupervised learning framework of Generative Adversarial Network (GAN), and leveraging their advantages in data generation, we propose an Auxiliary Discrimination Mechanism-based Generative Adversarial Network (ADM-GAN) for the task of traffic flow data restoration. The optimization of the generator and discriminator and the auxiliary discrimination matrix, constructed from the generator’s output and the original data features, enhances the model’s utilization of original data. We tested our model using ETC traffic flow data from the G50 freeway near Suzhou, China. The results demonstrate that ADM-GAN outperforms other state-of-the-art methods in data restoration tasks across various data missing rates. This research offers an efficient and reliable method for freeway traffic data restoration.

The accurate prediction of traffic conditions is essential for effective and efficient traffic management and control. The dynamic and complex nature of traffic data, characterized by intricate temporal and spatial features, presents significant challenges to accurate traffic forecasting. While previous studies have developed various models with advanced algorithms, they often fail to fully capture the holistic spatio-temporal features and the dynamically evolving correlations within traffic networks. Additionally, these studies often overlook the potential of adjacency matrices learned from real-time traffic data to more accurately represent the interconnectivity of nodes within road network. To address these gaps, this study introduces the Generalized Dynamic Spatio-Temporal Graph Convolutional Network (GDSTGCN), a novel prediction model tailored for traffic data. First, this model builds a learning-based generalized dynamic graph structure, which incorporates both spatial and temporal connections and evolves with real-time traffic data. Then, a generalized dynamic graph convolution, integrated with graph diffusion, is crafted to operate on the designed generalized dynamic graph structure. This plays a critical role in holistically capturing local and global spatio-temporal traffic dependencies. Moreover, the generalized dynamic graph convolution is incorporated with Temporal convolution and other essential components, forming a cohesive framework that enables effective and efficient traffic flow predictions. To validate the performance of the GDSTGCN model, we conducted extensive experiments using four real-world road network datasets. The results demonstrate that our model outperforms existing state-of-the-art GCN-based models and traditional baseline methods.

Accurately predicting the probability of crashes is crucial for preventing traffic crashes and mitigating their impacts. However, the imbalance in crash data, irregular road network structures, and heterogeneity in multi-source data pose significant challenges. To address these issues, this study introduces a spatio-temporal graph convolutional network traffic crash prediction model based on Voronoi diagrams that considers geographical spatial distribution. Initially, this study introduces a spatial partitioning method based on Voronoi diagrams, grounded on the geographic spatial distribution characteristics of traffic crashes. It constructs a novel graph structure with spatial units within Voronoi diagrams as nodes and the shared length of different road types between units as edges. This graph structure integrates the spatial distribution characteristics of crashes with the graph structure, substantially contributing to addressing the zero-inflation problem inherent in spatial units constructed on a grid basis. Subsequently, the study employs a GCN (Graph Convolutional Network) and Transformer encoder to build the VSTGCN (Voronoi-Based Spatio-Temporal Graph Convolutional Network) crash prediction model, evaluating its effectiveness using real data from New York City. Comparisons with eight baseline models demonstrate that VSTGCN outperforms them in all evaluation metrics. Moreover, the paper conducts model ablation studies from different perspectives, such as feature modules and graph structure composition, revealing that the chosen spatial, temporal, and spatio-temporal features significantly influence the model’s predictive performance, with spatial features having the most substantial impact. Finally, the novel graph structure based on Voronoi diagrams proposed in this study shows a clear advantage in model effectiveness compared to traditional graph structures. This research can effectively handle complex crash data structures and accurately predict crash probabilities, providing a reliable basis for developing measures to prevent crashes and alleviate their impacts.

This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle’s acceleration and steering angle. The statistical analysis of the model’s parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this strong correlation should not be ignored if one is interested in the vehicle’s microscopic motion behavior. We further develop a driving risk potential field-based car-following model (DRPFM) to remedy the failure of acceleration consideration under the conventional environment, whose parameters are calibrated by filtered I-80 NGSIM data with frequent traf?c oscillations. Simulation results indicate that our proposed DRPFM model is proved to be a good description of car-following behavior and outperforms two classical car-following models (Optimal Velocity Model and Intelligent Driver Model) in frequent oscillation phases due to our consideration of potential acceleration data acquisition in real-time under the CAVs environment. In addition, this DRPFM model is applied to deduce the safety conditions for vehicle lane-changing. The analysis results prove that this model can reasonably explain the influencing factors between driver types and lane-changing safety conditions in practice.

In the conventional variable speed limit (VSL) strategy, the control area is fixed under confined conditions. With the facility of the roadside unit, the control area for the VSL will vary in real-time in the connected environment. This study proposed an extended model-based VSL controller to improve traffic efficiency in the connected environment. The controller was designed based on the scheme of model predictive control (MPC). In the controller, an extended cell transmission model (CTM) with variable-length cells was established. The cell length variation was introduced to describe the characteristics of the variable control area. The optimizer in MPC is an improved Genetic Algorithm. A numerical simulation was conducted to show how the method, with the cooperation of variable speed limit and variable control area, alleviates the shock waves. The performance of the method was compared with a conventional VSL without a variable control area. The results show that the extended model-based VSL controller reduces the total travel time by 14.57% compared with the conventional VSL controller. The compared results illustrate that the proposed VSL controller can effectively resolve shock waves produced by the incident.