Abstract
Big Railway Data, such as train movement logs and timetables, have become increasingly available. By analyzing these data, insights about train movement and delay can be extracted, allowing train operators to make smarter train management decisions. In this paper, we study the problem of performing long-range analysis on Big Railway Data, such as estimating the remaining journey time, i.e., the amount of time for a given train to reach the terminal station. We study how existing statistical and machine learning methods, designed for short-range analysis (e.g., estimating the traveling time between two adjacent stations), can be extended to perform long-range analysis. We further design a method, called a-LSTM, based on LSTM (long short-term memory) neural network and attention models. Extensive evaluation on a large amount of train movement data provided by a train service provider in Hong Kong shows that a-LSTM is more effective than other solutions in predicting traveling times.
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Acknowledgement
Wenya Sun and Reynold Cheng were supported by MTR (project 200009153) and the University of Hong Kong (Projects 104005858, 10400599, 207300392). Tobias Grubenmann was supported by the Federal Ministry of Education and Research (BMBF), Germany, under Simple-ML (01IS18054), and the European Commission under PLATOON (872592) and Cleopatra (812997). We would like to thank the MTR Corporation, especially Mr. Leo Cheng, for providing their data and advice. We thank Huawei corporation (and Ms. Kathy Ng) for providing a high-performance server for our study. We also thank Prof. W. K. Li, Prof. Philip Yu, and Mr. W. K. Kwan for their valuable suggestions in the early phase of this work.
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Sun, W., Grubenmann, T., Cheng, R., Kao, B., Ching, W. (2022). Modeling Long-Range Travelling Times with Big Railway Data. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_38
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DOI: https://doi.org/10.1007/978-3-031-00129-1_38
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