To this day, railway actors obtain information by actively hunting for relevant data in various places. Despite the availability of a variety of travel-related data sources, accurate delivery of relevant, timely information to these railway actors is still inadequate. In this chapter, we present a solution in the form of a scalable software framework that can interface with almost any type of (open) data. The framework aggregates a variety of data sources to create tailor-made knowledge, personalised to the dynamic profiles of railway users. Core functionality, including predefined non-functional support, such as load balancing strategies, is implemented in the generic base layer, on top of which a use case specific layer – that can cope with the specifics of the railway environment – is built. Data entering the framework is intelligently processed and the results are made available to railway vehicles and personal mobile devices through REST endpoints.
Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization
