Abstract
Rail transportation is a convenient and safe in many countries. However, Rail transportation in some countries has significant long delays. Arrival time prediction and rescheduling the time table are partial solutions to tackle the delay problem. In this paper, the relationship between measurable properties and the delay time are studied in order to develop an arrival time prediction. The result of this experiment has three parts. The relationship between properties and arrival late are then visualized and discussed. Some properties from the acquired database show that week, day and station, are important features and impact on the delay. Various regression methods are compared in our experiment and the result shows that best RMSE is ± 3.863 min by applying Random Forest Regression on train tracking dataset.
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References
Li, F., Sheu, J.-B., Gao, Z.-Y.: Deadlock analysis, prevention and train optimal travel mechanism in single-track railway system. Transp. Res. Part B Methodol. 68, 385–414 (2014)
Kecman, P., Goverde, R.M.P.: Online data-driven adaptive prediction of train event times. IEEE Trans. Intell. Transp. Syst. 16(1), 465–474 (2015)
Pongnumkul, S., Pechprasarn, T., Kunaseth, N., Chaipah, K.: Improving arrival time prediction of Thailand’s passenger trains using historical travel times. In: 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 307–312 (2014)
Marković, N., Milinković, S., Tikhonov, K.S., Schonfeld, P.: Analyzing passenger train arrival delays with support vector regression. Transp. Res. Part C Emerg. Technol. 56, 251–262 (2015)
Yaghini, M., Khoshraftar, M.M., Seyedabadi, M.: Railway passenger train delay prediction via neural network model. J. Adv. Transp. 47(3), 355–368 (2013)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É.: Scikit-learn: machine learning in Python, arXiv:1201.0490 Cs (2012)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Acknowledgement
We would like to very thank Mr. Chokdee Suwanrat and his department of information technology and State Railway of Thailand for providing the data and consulting on a workflow of train operations and many definitions of the technical term.
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Kosolsombat, S., Limprasert, W. (2017). Arrival Time Prediction and Train Tracking Analysis. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_15
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DOI: https://doi.org/10.1007/978-3-319-60675-0_15
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