Intelligent Train Automatic Stop Control (iTASC)

In a conventional train signalling system, stopping a train at stations is the responsibility of train drivers. Before each station, a signal known as Home Signal in railway terminology, warns the driver that the train is approaching a station. However, due to different brake system characteristics and capabilities, different track profiles as well as different competency levels of drivers, it is a challenging task to stop a train precisely by just one braking action while maintaining a uniform quality of ride.

In addition to this, the use of platform screen doors (PSD) in railway stations can introduce various challenges for planners, track engineers, rolling stock manufacturers, brake engineers and PSD suppliers. Monitoring stopping spots, the braking rate, and real data are the initial requirements for any further development and evaluation for a sound and stable train control system.

In the last three decades, train automatic stop control (TASC) algorithms have been developed and applied to different metro and heavy haul rail corridors all over the globe. However, even the most developed controllers have relied entirely on station markers such as home signals, on-the-track sensors or Balises. Although, position uncertainty has been considered in several studies before, it has been largely ignored in TASC studies so the foremost shortcoming of previously developed TASC algorithms is that they had not considered position uncertainty. The second most important problem with these algorithms for TASC is the exclusion of the inherent time delay in braking systems in response to any control signal.

Therefore, to consider those factors, a braking model for station stopping is developed in this thesis, which accounts for the time dependency of the train’s air brake system to improve the accuracy of the train’s stopping.

Finally, train position uncertainty, which is a missing concern in previous works, has been added to this thesis’s study.