Providing data analysis insights into real to-the-second timing patterns of passenger rail services using Machine Learning techniques
Middlesex University and University of Brighton
With support from Southeastern
This project investigated the use of Machine Learning techniques to provide useful data analysis and insights into passenger rail services within the Southeastern network. The emphasis was on providing more accurate models and estimates about station dwell times and between station track section travel times, including real time prediction, variation and correlation to temporal, geographical and external factors.