## 2 Literature Review
### Eco-Driving Approaches
Furthermore, artificial intelligence techniques such as Artificial Neural Networks (ANN) or Nature Inspired algorithms are also usual techniques in the train eco-driving field. The use of ANN is illustrated in [46] where the ANN model is trained using detailed simulation and, in combination with a Genetic Algorithm (GA), the most energy efficient train travel between stations for a running time is obtained for a mass transit system. ANN was also applied in [47] using data of real mass transit operation to the training. The model obtained is used to analyse and design efficient train control algorithms. The capabilities of GA solving the eco-driving problem was demonstrated in [48] where this algorithm was used to optimize the train speed profile between stations using an appropriate coast control that can be implemented in ATO. In [49], GA was applied to find the coast control that minimizes the energy consumption between stations for a given running time. In this work, GA was demonstrated as a robust technique compared with classical search methods. A single train trajectory optimization model was presented in [50] to minimize the train energy consumption and maximize the regenerative energy usage. The optimization is performed by means of a GA that makes use of a multi train simulation model to obtain the energy consumption of a single-train journey and the regenerative energy exchange among trains. GA is the core algorithm of Optidrive, the tool presented in [51], which is capable of finding a table that shows where a train has to accelerate, coast, and decelerate to minimize the energy consumption in a travel between two stations. The tool is proposed to compute ATO speed profiles and to assess the impact of driving style in the energy consumption. A jointly optimization process for timetable and speed profile generation by means of GA is presented in [52] and applied to a metro line. The method provides results to demonstrate that the joint optimization process obtains solutions with lower energy consumption compared with the two-step optimization of speed profile and timetables. GA has been used in combination with fuzzy logic with different purposes. In [53], a GA is applied to study the trade-offs between energy consumption and running time in a single-train journey modifying using a fitness function with variable weights for energy and time. In this case, fuzzy logic is included in the optimisation process to enable a more intelligent search and to allow the representation of specific punctuality requirements. A combination of GA and fuzzy logic can be found in [54; 55] to reduce the energy consumption of a high-speed train operation taking into account the uncertainty associated to manual driving. The research presented in [55] is focused on the development of a method that can be online applied to recover delays during the journey in an energy efficient manner, while [54] is focused on obtaining the optimal timetable using eco-driving. Ant Colony Optimisation (ACO), GA, and Dynamic Programming algorithms are applied to a distance-based train trajectory model to search for the optimum train speed trajectory in [56]. An application of Differential Evolution (DE) is applied in [57] to study the energy result variation when the running time between successive stations of a train journey is modified. The research presented in [58] makes use of a combinational MAX-MIN ACO algorithm to achieve an energy optimal train driving between two successive stations constrained by the mean speed value of the journey. The authors analyse the calculation time of the proposed method and conclude that it can be used online in a mass transit system. Simulated Annealing (SA) is applied in [59] to search for the coasting position that minimizes the train energy consumption for a target running time between stations.