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Civil-Comp Conferences
ISSN 2753-3239 CCC: 7
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo
Paper 10.1
Short-Term Rainfall Prediction Method Considering Orographic Rainfall for the Train Operation Control Y. Nakabuchi1, T. Shinomiya1 and E. Nakakita2
1Research and Development Center of JR East Group, East Japan Railway Company, Tokyo, Japan
Y. Nakabuchi, T. Shinomiya, E. Nakakita, "Short-Term Rainfall Prediction Method Considering Orographic Rainfall for the Train Operation Control", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on
Railway Technology: Research, Development and Maintenance",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 7, Paper 10.1, 2024, doi:10.4203/ccc.7.10.1
Keywords: train operation control, heavy rainfall, train stopping between stations, rainfall prediction, advection model, orographic rainfall calculation method.
Abstract
To ensure safe train operation in heavy rainfall, railway operators enforce “the train operation control” such as speed reduction and stopping, based on observed precipitation by rain gauges. In the train operation control, trains need to stop immediately when the rainfall value reaches standard values of stop, even if the train is running between stations. When trains stop between stations, it is necessary to stop until rainfall calm down and the surrounding area is confirmed to be safe, and passengers may be kept in the train for a long time. If there is a highly accurate rainfall forecast information, we could know in advance the time when the train operation control will be issued, and stop trains at stations. In this study, as a short-term rainfall prediction method for this objective, we developed a method combining the advection model and the orographic rainfall calculation method. We verified accuracy of predicting the train operation control issuance times by the developed method using multiple rainfall cases. As a result, it was found that in a high probability of about 80% for predictions 10 minutes ahead and about 70% for predictions 20 minutes ahead, the method could predict the issuance time accurately.
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