Intelligent use of movement and shock data from freight wagons

This is a cooperative project between TU Darmstadt and Deutsche Bahn AG. Students participating in the project have access to datasets (CSV files) in the 220 GB database. This dataset includes sensor data recorded from Deutsche Bahn wagons. Sensor data includes van ID, geolocation, speed, driving status, GPS signal quality, cellular signal quality for positioning, battery information, and various time stamps. Students should first explore data and present statistics. Then do data mining and answer a few questions about DB Cargo business. Finally, machine learning tools should be used to identify the operating status of the truck: arrival, parked, shunted etc. inside the train stations, etc. within the train station. Use numpy, pandas, matplotlib, folium and other modules and K-means clustering, Gaussian mixture method clustering and other methods to develop Python programs.