Regional trains Berlin-Brandenburg (RE3 and RE5)

ProTrain aims to create novel services or significantly improve existing services through a merger of data and data provided in the mCLOUD. The aim is to effectively guide passengers before and during the use of public transport to improve the use of existing capacities and resources in public transport. With the help of the forecasts and the current occupancy levels, passengers are to be guided in such a way that the balance between supply and demand is as balanced as possible. Thus, a passenger looking for a connection can be advised to use an earlier or later connection to avoid large crowds on the train. For this purpose, requirements analyses were carried out, which identifies relevant user groups and stakeholders with their specific requirements. An overall technical system that met these requirements was created, a data platform was set up, and a modular system landscape was specified that accessed heterogeneous data sources and whose data was integrated and provided as a service. Special attention was paid to the conception of efficient data management for historical data. Suitable interfaces for data exchange between the suppliers have been defined. For the test user information, a web app was considered the best-suited solution and, adapted to the system, was designed. With the help of a specially developed SDK on Android phones, the means of transport used should be determined for some subjects. These were user-confirmed. The specification of the service has been developed, which defines the required data for the applications. Three different forecasting algorithms were developed for certain forecast periods. The data interface strategy was developed, the components of the interfaces agreed between the partners, the travel archive made available online. Algorithms for the automatic detection of means of transport and purpose of travel have been developed. On the basis of passenger counting data and seating capacity, utilisation levels were determined. The assignment data from AFZS for RE trains were provided to the project. The extension of the timetable data management to include weather data has been carried out. The basic forecast was differentiated according to weather conditions (e.g. summer day). Day groups, time of day and journey section have been calculated and provided. A service concept has been created for forecasting information to test users. A web app on the HaCon platform was developed as a test app. The enclosed dataset includes the consolidated forecast based on the three algorithms (column N) and actual utilisation (column O). Column E and F describe the journey from up to. The columns J-M provide information on the respective section. The dataset contains all data for a calendar day (November 1, 2019).