Automatic Train Control Algorithms with Regulation Restrictions Adaptive to System State Changes

Abstract Centralized automatic train control algorithm is proposed which allows effective adjustment of each train’s departure and running times or intervals when there are unscheduled delays. The novelty lies in that it employs three sets of methods, called regulative characteristics, which contain relationships between regulation restrictions (minimal station dwell times and train running times). These relationships are obtained a priori from the simulation of train circulation on the detailed railway or metro line model, saving later onboard real-time processing requirements, which is especially useful for those train control systems which are being only partially modernized. In the traditional algorithms these regulation restrictions are frequently considered fixed, while the changes of system’s state do affect them, so the usage of the values recurrently updated by the regulative characteristics allows to have more flexible running and station times for every train on the line. The efficiency of the new algorithm is additionally increased by the usage of forecasting of station dwell times and train running times based on the history of these values for each train or station. Proposed algorithm decreases the quantity of undesired stops between stations, allowing to save energetic resources spent on repeated train acceleration and to raise, in general, the quality of the transportation process algorithm has been tested on Mexico Metro Line simulator confirming efficiency increase of 3% for the worst case scenario and up to 10% for the best cases (busiest state of the line plus additional temporal speed limits).

Keywords— Railway transportation, Automatic Train Control, Metro, Regulation Algorithms, Centralized Control, Forecasting.

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