Malatya Public Transportation Route Optimization via Ant Colony Algorithm


International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 28 - 30 September 2018 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Malatya
  • Country: Turkey


Increasing population density causes traffic densities in city centers. In this study, Ant Colony Algorithm (ACO) was used to find solutions to the traffic problems in crowded cities and Malatya province was chosen as the application region. Need of reducing the traffic intensity in the city centers, has led to the idea that the central stop of public transportation vehicles should be moved. This situation reveals the problem of changing the routes of public transport. In this study, ACO algorithm was used to analyze the new routes in the most ideal way. It is aimed to realize minimum distance and minimum traffic density by solving this problem which is similar to the traveling salesman problem. In order to achieve minimum traffic intensity, the threshold pheromone amount is determined to direct multiple vehicles to alternative routes. The data used in the analysis belongs to the public transportation vehicles of the city of Malatya. A java based program was used to construct the datasets and to solve the problem.