A scalable software framework for real-time data processing in the railway environment

Today, railway actors (including passengers and crew members) obtain information by actively hunting for relevant data in various places - manually linking their personal profile with public transportation time tables, floor plans from railway and underground stations, weather forecasts, etc. Despite the availability of a variety of travel-related data sources, accurate delivery of relevant, timely information to these railway actors is still inadequate. This paper presents a solution to the abovementioned problem in the form of a scalable software framework that is able to interface with any type of (open) data. The framework aggregates a variety of data sources to create tailor-made knowledge, tuned to the dynamic profiles of railway users. The needs of these railway users were extracted using novel user experience techniques in order to determine the requirements of this framework. The resulting OSGi-based framework architecture is two-layered. Core functionality, including predefined load balancing strategies, is implemented in the generic base layer, on top of which a use case specific layer - that is able to cope with the specifics of the railway environment - is built. Data entering the framework is intelligently processed and the result is made available to railway vehicles and mobile devices through REST endpoints.