AI improves the quality of passenger information in public transport
Dr. rer. nat. Christine Langhanns, Head of the Data Analytics Team, and Dr. rer. nat. Kolja Thormann, Data Scientist, both of rms GmbH, demonstrate how artificial intelligence can help improve the quality of real-time forecasts in public transport in their latest expert article in the journal DER NAHVERKEHR.
The focus is on how AI-supported methods can enhance the quality of forecast data and thereby improve passenger information.
Reliable travel information is a key component of an attractive public transport system and, consequently, of environmentally sensitive mobility management. In the AIAMO research project, the authors investigate how large volumes of real-time data can be analysed automatically in order to systematically assess the quality of departure forecasts. The analysis is based on forecast and actual data from Leipziger Verkehrsbetriebe (LVB).
The article 
describes how machine learning methods are used to distinguish between plausible and implausible forecast messages. The aim is to identify incorrect information at an early stage, thereby laying the foundations for more accurate passenger information, better journey planning and increased appeal of public transport.
At the same time, the results demonstrate the potential of AIAMOnexus: by intelligently linking further mobility and environmental data, forecasting models can be continuously refined in future. In this way, AIAMO lays the foundations for continuously improving the quality of passenger information and establishing public transport as a key component of data- and AI-driven, environmentally sensitive mobility management.
You can read the full expert article in the July issue of DER NAHVERKEHR. (PDF)
