Mining Public Transport Data
See here for the code. During the summer semester 18, I had the opportunity to take part in the practical course ‘Large-scale Machine Learning’ offered by Prof. Guennemann at TUM. In my team, consisting of four people, we worked on mining public transport data in Munich provided by the company running the buses and trams (MVG). Almost every household in Munich is located within 400 meters of an underground or tram station or a bus stop. The resulting network adds up to more than 587 km of routes for buses and trams only, which are served by 461 vehicles that stop approximately 345,000 times a day and log their position and status every few seconds. ...