Optimized schedules for busses and trains combined with active flow management of passenger crowds could significantly improve traveling by public transport. To test different scenarios the project team uses a mock-up of the Hamburg-Harburg train station. This "digital twin" is fed with all the train station data, e.g. timetables, weather, events and emergency situations. AI helps to manage the huge amount of data. Daniel Plöger explains what this means for travelers: "If a lot of people use a staircase or if it is closed due to construction work, the AI should automatically determine that a slightly longer route is a better way to your destination. This also takes into account weather changes or expected delays." On a larger scale data from social media, ticket sales etc. could be used to predict a higher demand for public transport more precisely and plan accordingly.
"This is a great example for a seemingly small improvement that has a big impact", says Daniel Plöger.
Read the full article in spektrum magazine here.
