Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning methods known for providing successful outcomes in the literature, and their success rates are compared. After training and testing the dataset with Gradient Boosting Regressor, Random Forest Regressor, and Support Vector Regressor methods, the Random Forest Regressor model achieved the highest success rate with an R2 of 0.98. However, due to its lower interpretability, the Polynomial Regression model was preferred for its higher interpretability despite slightly lower accuracy.
DOI
10.55730/1300-0632.4167
Keywords
Machine learning, railway, signalization, software, speed
First Page
137
Last Page
163
Publisher
The Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
AKÇAY, M. T, & AKGÜNDOĞDU, A (2026). Improving rail system signaling efficiency through ai-based driving profile generation: a comparative performance analysis. Turkish Journal of Electrical Engineering and Computer Sciences 34 (1): 137-163. https://doi.org/10.55730/1300-0632.4167
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Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
