A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.

Route finding is one of the primary functions of a car navigation device. Usually, one of the criterion i.e. the minimum traveling time, distance or safety etc. is selected to find the optimal route. Instead of this, a multi-criteria route planner that considers driver preferences at the same time would be more preferable. In this study, a route planning strategy that considers road safety and travel time as user selected criteria is applied. Normal pairwise comparison method and regular increasing monotone quantifier-guided ordered weighted averaging operators are used for integrating the driver preferences (e.g. relative importance and decision strategy) to form a cost for each link of a network. The proposed approach is tested for a specific region of Odunpazari district near Eskisehir Osmangazi University (ESOGU) in Eskisehir. Travel time costs of links are calculated considering the lengths and speed limits in the region. Safety costs of links are determined considering a set of parameters such as road width, number of lanes in the road, road slope, road surface type and weather conditions.

Diabetes is a lifelong disease that has undesirable effects on various organs, such as long-term organ damage, functional disorder, and finally failure of the organ. Diabetes must be treated under the supervision of a doctor. Diabetes is known as a disease that can be seen in many people today and is becoming widespread due to life conditions. If a person with diabetes does not receive any treatment at an early stage, the patient's body can react with serious complications. In addition to the medical methods used in the diagnosis of diabetes, this disease can be detected by an artificial intelligence approach. This research aims to establish the most influential variable among the many variables causing diabetes and to design a model that will predict diabetes to help doctors analyze the disease with selected machine learning methods. In this study, Decision Tree, Bagging with Decision Tree, Random Forest and Extra Tree algorithms were used for the proposed model and the highest accuracy values were obtained with the Extra Trees algorithm with 99.2%.

Increasing demand for energy and environmental concerns have increased importance of energy-efficient transportation. In this manuscript, an energy-efficient train operation based on finding optimal speed profile is studied. As a nature-inspired metaheuristic approach, Firefly algorithm is employed to find optimal switching points of the train control signal. In problem formulation, energy consumption is taken as major part of objective function and travel time is being included as penalty factor. In order to verify the obtained results, a simulation is performed. Besides firefly algorithm, genetic algorithm is also used to compare results. Two algorithms are simulated on test track with various grade profiles for several times. Both of them considered four phases (maximum acceleration, cruising, coasting and braking) of train motion. Furthermore with the help of relaxing boundary conditions, algorithms are able to organize motion phases excluding cruising phase. Simulation results demonstrated that, compared to the GA, FA provides more accurate and persistent solutions. In addition, it can be converged to solution in small iterations, so it is compatible for using in real time problem solving.