Ant colony algorithm for rational transit network design of urban passenger transport

This study presents an optimization model for a transit network design of urban passenger transport. It aims to maximize the number of direct travelers per unit length, that is direct traveler density, subject to route length and nonlinear rate constraints (ratio of the length of a route to the shortest road distance between the origin and destination). Ant colony optimization algorithm is one of the possible meta-heuristic approaches, which are used to find an optimal route by using the graphs. The essence of this method is that its model derived from the study of the real ants behavior as the creation of the algorithm was inspired by these invertebrates. Data collected in Tomsk, Russia, are used to test the model and the algorithm. The results show that the optimized transit network has significantly reduced transfers and travel time. They also reveal that the proposed algorithm is effective and efficient compared to some existing meta-heuristic algorithm.