Network-Wide Optimal Scheduling of Transit Systems Using Genetic Algorithms

The primary objective of any transit system is to provide a better level of service to its passengers. One of the good measures of level of service is the waiting time of passengers during their journey. The waiting time consists of an initial waiting time (the time a passenger waits to board a vehicle at his or her point of origin) and a transfer time (the time a passenger waits at a transfer station while transferring from one vehicle to another). An efficient schedule minimizes the overall transfer time (TT) of passengers transferring between different routes as well as the initial waiting time (IWT) of the passengers waiting to board the vehicle at their point of origin. This paper uses genetic algorithm (GA)—a search and optimization procedure—to find optimal/near-optimal schedules of vehicles in a transit network. The main advantage of using GA is that the transit network scheduling problem can be reformulated in a manner that is computationally more efficient than the original problem. Further, the coding aspect of GA inherently takes care of most of the constraints associated with the scheduling problem. Results from a number of test problems show that GAs are able to find optimal/near-optimal schedules with minimal computational resources.