Improvement of timetable robustness by analysis of drivers' operation based on decision trees

Abstract

In railways where trains are running densely, once there occurs a delay, even if it is small, the delay easily propagates to other trains. In order to make their timetables more robust, railway companies are making various kinds of efforts. But until now, they have not been interested in analysis of drivers’ operation, although there exists much difference in their manner of driving and the difference is closely related with robustness. Thus, it would be useful if we can know what is “good driving”, in other words, a driving which reduces a delay and what is “poor driving” meaning a driving which increases a delay. If we can know the difference between “good” and “poor” driving, we can give advice to drivers so that they can improve their driving. We have developed an algorithm to find the factors which differentiate between “good” and “poor” driving based on the decision tree, which is a commonly used technique in data mining. The inputs of our algorithm are track occupation records. The algorithm receives “good” examples and “poor” examples as input, then it produces a decision tree from which we can know the dominant factors to differentiate between the good examples and the poor examples. We have applied our algorithm to actual data and proved that the algorithm can find a pattern of driving which is common to poor drivers.