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 multisource 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 GAN (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 electronic toll collection 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.

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.

In connected and automated environments, implementing feedback control on selected key connected and automated vehicles within a platoon can indirectly influence the operation of human-driven vehicles, ultimately optimizing overall traffic efficiency and safety. To enhance global traffic flow through centralized control of essential vehicles, an improved spectral clustering-based pinning control (ISC-PC) strategy is proposed in this paper. The strategy considers communication topology, vehicle individual features and vehicle interactive features to identify key control nodes, targeting them for pinning control to enhance traffic flow. A control algorithm is developed for these pinning vehicles, incorporating communication topology and vehicle dynamics, with a dual focus on safety and efficiency. Furthermore, the proposed ISC-PC strategy is validated through numerical simulations, employing both synthetic and real-world datasets, which include typical noises in traffic flow. Control effects are analyzed and compared under various pinning node identification methods and pinning rates. Experimental results indicate that the ISC-PC strategy is highly effective in reducing traffic oscillations and improving traffic efficiency and safety, underscoring its potential to maximize control effects under constrained control conditions.

Traffic sensors provide real-time measurements of current traffic conditions, whereas traffic management applications require forecasts of future traffic speed or flow. This study considers the incremental expansion of an operating sensor network, in which a pre-trained network-level forecasting model must predict traffic states at sensor locations that were absent during training. Many spatio-temporal graph neural networks rely on sensor-specific embeddings and graph connections learned from data-rich training networks. These representations are undefined for previously unseen locations, limiting the direct application of pre-trained adaptive-graph forecasters during sensor-network expansion. To address this cold-start problem, we propose support-conditioned sensor-adaptive meta-graph learning (SC-SAMG), which derives target-node representations and spatial dependencies from a short support period. The framework combines a support-set encoder, a task-specific graph learner, and first-order meta-learning to adapt the network-level forecaster using one to seven days of target observations. Experiments on four traffic benchmarks evaluate forecasts of speed or flow over the next 15–60 min under a leakage-controlled held-out-node protocol. SC-SAMG consistently outperforms fine-tuned gated recurrent unit (GRU), adaptive graph convolutional recurrent network (AGCRN), and diffusion convolutional recurrent neural network (DCRNN) baselines. It reduces mean absolute error (MAE) by up to 11% relative to the adaptive-graph baseline and by up to 7% relative to the diffusion convolutional baseline. These results demonstrate the potential of support-conditioned graph adaptation for incorporating previously unseen sensor locations into existing network-level traffic forecasting systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.