Optimization of a Transit Services Model with a Feeder Bus and Rail System Using Metaheuristic Algorithms

Abstract

Nowadays, many passengers use transit systems to reach their destinations; however, the growing concern for public transit is its inability to shift passenger’s mode from private to public transportation. By designing a well-integrated public transit system and improving the cost-effectiveness network, the public transport could play a crucial role in passenger satisfaction and reducing the operating cost. The main target of this paper is to present a new mathematical programming model and design an efficient transit system to increase the efficiency of integrated public transit services through the development of feeder bus services and coordination of major transportation services with the aim of minimizing the costs. In this study, optimized transit services and coordinated schedules are developed using metaheuristic algorithms such as genetic algorithm, particle swarm optimization, and imperialist competitive algorithm. The data used and the coordination were obtained from a case study widely provided in the literature. Finally, obtained numerical results of the proposed model including optimal solution, statistical optimization results, and the convergence rate, and comparisons are discussed in detail using tables and figures.