China’s widely adopted expressway ETC system provides a feasible foundation for realizing co-operative vehicle–infrastructure integration, and the accuracy of ETC data, which forms the basis of this scheme, will directly affect the safety of driving. Therefore, this study focuses on the abnormal data in an expressway ETC system. This study combines road network topology data and capture data to mine the abnormal patterns of ETC data, and it designs an abnormal identification model for expressway transaction data based on TL-XGBoost. This model categorizes expressway ETC abnormal data into four distinct classes: missing detections, opposite lane detection, duplicated detection and reverse trajectory detection. ETC transaction data from a southeastern Chinese province were used for experimentation. The results validate the model’s effectiveness, achieving an accuracy of 98.14%, a precision of 97.59%, a recall of 95.44%, and an F1-score of 96.49%. Furthermore, this study conducts an analysis and offers insights into the potential causes of anomalies in expressway ETC data.

In Electronic Toll Collection (ETC) systems, accurate gantry topology data are crucial for fair and efficient toll collection. Currently, inaccuracies in the topology data can cause tolls to be based on the shortest route rather than the actual distance travelled, contradicting the ETC system’s purpose. To address this, we adopt a novel Gradient Boosting Decision Tree (GBDT) algorithm, Light Gradient Boosting Machine (LightGBM), to dynamically update ETC gantry topology data on highways. We use ETC gantry and toll booth transaction data from a province in southeast China, where ETC usage is high at 72.8%. From this data, we generate a candidate topology set and extract five key characteristics. We then use Amap API and QGIS map analysis to annotate the candidate set, and, finally, apply LightGBM to train on these features, generating the dynamic topology. Our comparison of LightGBM with 14 other machine learning algorithms showed that LightGBM outperformed the others, achieving an impressive accuracy of 97.6%. This methodology can help transportation departments maintain accurate and up-to-date toll systems, reducing errors and improving efficiency.

The rapid generation and automatic updating of road network topology information have immense practical significance and application value for intelligent expressways. Therefore, this work proposes a novel arch-bridge topology based on an electronic toll collection (ETC) system to redefine the structure of the expressway network. On this basis, discrete ETC data generate corresponding trajectories according to the arch-bridge topology structure, and an initial topology candidate set is generated. Furthermore, the characteristics of abnormal topologies, such as loop, reverse, missing, and opposite topologies, are deeply explored and analyzed, and targeted constraint conditions are designed to optimize the initial topology candidate set. Finally, experiments and evaluations are conducted on transaction data collected from an ETC system in a certain province of China. The experimental results show that the proposed method is practical and has significant results in terms of evaluation metrics and efficiency. Specifically, the recall, precision, and F1 are 0.982, 0.966, and 0.974, respectively. Additionally, it takes less than 2 ms on average to generate one topology. The proposed method can efficiently and accurately generate the topology information of an entire expressway network.