Journal of the Eastern Asia Society for Transportation Studies
C: Travel Demand Analysis and Forecast
Validating an Improved Model for Feeder Bus Network Design Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)
Mohammad Hadi AlmasiSina Mirzapour MounesMohamed Rehan Karim
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Volume 11 (2015) Pages 507-522

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Abstract

There is an increasing population rate especially in metropolitan areas and the majority of people living in such areas have to spend some part of their time commuting to their destinations. So, one of the serious concerns for network design is defining an efficient and appropriate network being able to shift passenger's mode from private to public transportation properly. The public transport can have an effective role in deriving passenger satisfaction and reducing the operating cost by means of designing a well-integrated public transit system along with improving the cost-effectiveness network. The main goal of this study is to present an improved model for feeder bus network design problem by minimizing total cost. In this study, Genetic Algorithm (GA) and Particle swarm optimization (PSO) were employed to optimize feeder bus services. The case study and input data which have been applied through the current study were already used by Kuha & Perl (1989). Finally, obtained numerical results of the proposed model, including optimal solution, statistical optimization results, and the convergence rate as well as comparisons were discussed.

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© 2015 Eastern Asia Society for Transportation Studies
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