Big data in railway operations: using artificial neural networks to predict train delay propagation

Bosscha, E. (2016) Big data in railway operations: using artificial neural networks to predict train delay propagation.

Abstract:Advances in big data, data collection and machine learning have made it possible to apply new machine learning concepts to a wide array of problems. The aim of this thesis is to explore the possibility of predicting secondary delays in a railway network using a recurrent neural network. This can eventually be used to perform risk analysis and alternative evaluation combined with stochastic delay modelling. Empirical data from Irish Rail is used to test this method and verify the results. First a RailML data model is constructed containing infrastructure, timetable and rolling stock information based on multiple data sources from Irish Rail. Significant features are identified and extracted from this data model for around 60.000 different delay combinations. Then a sequential approach is used incorporating a recurrent neural network to predict the total knock-on delay. This sequential approach allows for input data in variable lengths, avoiding information loss due to generalization of features. The model is trained with mini-batch gradient descent using the RMSprop algorithm on a large portion of the 60.000 training examples, and validated using the remainder of the example delay combinations. A coefficient of determination of R2 = 0; 7029 is achieved, which is comparable to similar machine learning methods presented in literature. The resulting accuracy is in the same order of magnitude as similar research using support vector machines. While results are less accurate then the results that can be achieved with micro-simulation tools, a series of improvements of the proposed method are presented which might be able to elevate the results of this method to a higher level.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Construction Management and Engineering MSc (60337)
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