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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.

Particle Swarm is an optimization method that is used for solving industrial problems and is highly preferred due to its ease of use and it's ability to find accurate results rapidly in recent years. In this study, it was used to optimize the resistance value of train sets. There are many types of resistance in train sets and the train can't start moving until the traction motors overcome the resistances. Run resistance, ramp resistance, and curve resistance are the resistances that the train must overcome at a constant speed. However, it is known that the acceleration of high-speed trains is very high and the resistance that the train sets must overcome for the change in speeds is acceleration resistance. This study aimed to calculate the acceleration, time, curve, ramp and distance, under certain constraints, for the total resistance value of YHT 65000 train by using the Particle Swarm Method as to obtain the minimum and maximum. Although, the results showed that the Particle Swarm Method returned very successful results for the minimum resistance, the same cannot be said for the maximum resistance.

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In Turkey, approximately 20% of the energy expended is spent on transportation. Light rail transportation technology is still in an evolving process. In this development process, it is crucial to have information about the energy consumption of the light rail vehicle according to the different situations of both the vehicle and the railway. It is necessary to predict the power losses that will occur under different driving conditions sensitively to ensure energy efficiency in light rail systems. The most important of these power losses is the resistance loss caused by contact with the route. Resistance loss is dependent multiple environmental conditions. The most important of these conditions can be listed as the weight of the light rail vehicle, the instantaneous speed of the vehicle, the curve of the route, the ramp slope of the route and the friction force arising from these conditions. Resistance loss is proportional and linearly dependent to some of these variables while others show reverse or nonlinear dependence. Due to these different types of dependencies, it is seen that a single multivariate nonlinear model is needed to explain the loss of resistance in all different conditions. In this study, a new and accurate model for resistance losses has been developed by fitting numerical values obtained from different scenarios to multivariate nonlinear regression model.