Artificial intelligence based bus routing in urban areas

Public transportation consists mostly of fixed transit systems, which have fixed stations, routes, and schedules. In this paper, a new approach for bus routing in public transportation is proposed, in which buses are traveling unbounded, adapting to passengers (not vice versa) by picking them up at their current location and transferring them to their destinations. Bus routes need to be adjusted to the passenger layout. This problem is close to the Dial-a-Ride Problem (DARP), but the solution is searched on real road-network graphs. The goal is to find a globally optimal set of paths for a given number of buses, such that all passengers are transferred to their destinations while the average travel time is minimal. In this paper, a modified Max-Min Ant System (MMAS) algorithm is utilized.