Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges.

The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway’s congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.

Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feature temporal attention mechanism in order to dynamically model the correlation of traffic flow in different time periods. Next, we adopt a spatial-temporal adaptive fusion graph convolutional network to capture the daily cycle similarity and potential spatial-temporal dependence of traffic flow data. Finally, the superiority of the proposed MFSTAPFGCN model over the traditional baseline model is verified through comparative experiments on real Electronic Toll Collection (ETC) gantry transaction data, and the effectiveness of each module is demonstrated through ablation experiments.

Traffic flow prediction is fundamental to traffic management and scheduling decisions in intelligent transportation systems. Traffic flow data exhibit complex spatiotemporal dynamics, multi-scale nonlinearities, and pronounced trend and seasonal patterns. Consequently, conventional statistical methods and univariate time-series models often struggle to balance predictive accuracy with computational efficiency. To address these challenges, we propose a multi-scale dynamic spatio-temporal graph trend-seasonality network (MS-DSTSN). First, we design a lightweight adaptive dynamic graph learning module. It leverages low-rank tensor decomposition and threshold-based sparsification to reduce graph construction complexity from O ( D 3 ) to O ( D K 2 ) . This reduction enables real-time capture of time-varying spatial correlations among nodes. Second, we develop a dynamic multi-scale spatio-temporal embedding module comprising three temporal scales and two spatial scales. We then employ an attention mechanism to adaptively fuse features across these scales. Third, we integrate a trend-seasonality decomposition module to explicitly separate traffic flow sequences into trend and seasonal components. This enhancement improves the model’s ability to recognize long-term periodic variations and abrupt fluctuations. Finally, we adopt an encoder–decoder architecture featuring a local encoder based on a gated recurrent unit and a global encoder based on a Transformer. This design captures both short-term dynamics and long-range dependencies. In multi-step forecasting experiments on real-world traffic datasets, MS-DSTSN achieves superior results across various evaluation metrics. Furthermore, ablation studies validate the effectiveness of each module. By balancing predictive accuracy, interpretability, and computational efficiency, MS-DSTSN offers a practical solution for large-scale urban traffic flow forecasting.

With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management.