Abstract
Delay prediction based on real-world train operation records is an essential issue to the delay management. In this paper, we present the first application of gradient boosting regression tress (GBRT) prediction model that can capture the relation between train delays and various characteristics of a railway system. Delayed train number (DN), station code (SC), scheduled time of arrival at a station (ST), time travelled (TT), distance travelled (DT), and percent of journey completed distance-wise (PC) are selected as the explanatory variables, and the delay time (WD) is the target variable. The model can evaluate various impact factors on train delays, which can assist dispatchers to make decisions. The results demonstrate that the GBRT model has a higher prediction precision and outperforms the support-vector machine (SVR) model and the random forest (RF) model.
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Acknowledgements
The authors gratefully acknowledge the support provided by the national key research project “Railroad Comprehensive Efficiency and Service Level Improvement Technology with High Speed Railway Network (Grant No. 2018YFB201403)” in China.
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Shi, R., Wang, J., Xu, X., Wang, M., Li, J. (2020). Arrival Train Delays Prediction Based on Gradient Boosting Regression Tress. In: Liu, B., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 640. Springer, Singapore. https://doi.org/10.1007/978-981-15-2914-6_29
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DOI: https://doi.org/10.1007/978-981-15-2914-6_29
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