Abstract
Rail transportation is an important part of the transport infrastructure that supports modern advanced economies. Both public and private companies are highly concerned on how travel patterns, vehicle-passenger behaviours and other relevant phenomena such as weather affect their performance. Usually any travel network can be remarkably expensive to build and swiftly gets saturated after its construction and any subsequent upgrades. We propose suitable workflow monitoring methods for developing efficient performance measures for the rail industry using business process workflow pattern analysis based on Case-based Reasoning (CBR) combined with standard Data Mining methods. The approach focuses on both data preparation and cleaning and integration of data applied to a real industrial case study. Preliminary results of this work are promising against the complexity of the data and can scale on demand while showing they can predict to an efficient accuracy. Several modelling experiments are presented, that show that the proposed approach can provide a sound basis for effective and useful analysis of operational sensor data from train Journeys.
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Bandis, E., Petridis, M., Kapetanakis, S. (2018). Business Process Workflow Mining Using Machine Learning Techniques for the Rail Transport Industry. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_37
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