A hybrid stochastic approach for train trajectory reconstruction

The next generation of railway systems will require more and more accurate information on train operation. These requirements are essential for the introduction of automatic processes into traffic management and train operation, the optimal use of infrastructure capacity and energy, and overall the introduction of data-driven approaches into rail operation. Train trajectories are a key information for operation, and their collection constitutes a primary source of information for offline procedures such as timetables generation, driving behaviour analysis and models’ calibration. Unfortunately, current train trajectory data are often affected by measurement errors, missing data and, in some cases, incongruence between dependent variables. To overcome this problem, a trajectory reconstruction problem must be solved, before using trajectories for any further purpose. In the present paper, a new hybrid stochastic trajectory reconstruction is proposed. On-board monitoring data on train position and velocity (kinematics) are combined with data on power used for traction and feasible acceleration values (dynamics). A fusion of those two types of information is performed by considering the stochastic characteristics of the data, via smoothing techniques. A promising potential use is seen especially in those cases where information on continuous train positions is not available or unreliable (e.g. tunnels, canyons, etc.).