Abstract
All the studies in urban railways gained importance with the requirement of developing in this area. However, not only technological development but also energy-saving conditions have great importance. One of these efficiency conditions is to know the optimum operating conditions. There are two electronic drive-warning systems. These warning systems are Driver Advisory System (DAS) and Automatic Train Operation (ATO), which are algorithm-based. To enrich these algorithms with meta-heuristic methods provides that it can be adapted to the changing operating conditions. Thus, flexible management can be achieved.
 In this study, the Particle Swarm meta-heuristic and Fmincon which is a nonlinear programming solver in MATLAB methods are used to calculate optimum driving speed, acceleration, cruising, coasting, and full braking times under different operating conditions. Comparative optimization results of these selected methods are presented. Thus, attention is drawn to the efficiency in driving technique with different optimization methods. Specific speed-specific driving time matches obtained can be used to develop innovative driving warning systems.
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