Improving Train Driving Performance under Disturbances by Intelligent Driver Advisory System

Unlike the trains in metro systems, trains in main line railway are operated by train drivers without automatic train control (ATO) systems. In addition, infrastructure, running environment and driver controller devices are also more complex and various. As a result, disturbances or even disruptions may influence the train driving process and disorganize normal train driving process. Driver Advisory System (DAS) has been studied and developed for many years with the main goal to guide train drivers towards better driving performance, and most efforts were put on energy-efficient and punctual driving under normal operational situations. This paper proposes an approach of intelligent DAS (iDAS) to assist train drivers improve driving performance under disturbance situations. Simulative experiments show that the proposed approach can effectively help the train drivers to be aware of, to handle and to recover to normal operation under typical disturbances.