A Swarm Based Method for Solving Transit Network Design Problem

In this study, a Discrete Particle Swarm Optimization (DPSO) algorithm is assimilated to solve the Transit Network Design Problem (TNDP). First, A Mixed Integer Model is developed for the TNDP. The solution methodology utilized here is made of two major elements. A route generation module is firstly developed to generate all the feasible transit lines. Through the second part, a DPSO algorithm is utilized to select the optimal set of lines from the constructed ones. The objective function is to maximize coverage index while satisfying the operator cost upper level constraints. The efficacy and accuracy of the implemented algorithms is compared with ones obtained by an enumeration process as well as an enumeration-based heuristic approach. Results confirmed that the PSO algorithm can find the optimum combination with significant decrease in the computational costs.

[1]  Gilbert Laporte,et al.  An Integrated Methodology for the Rapid Transit Network Design Problem , 2004, ATMOS.

[2]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[3]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[4]  Avishai Ceder,et al.  Transit-network design methodology for actual-size road networks , 2011 .

[5]  Wang Xi-huai.  Vehicle Routing Problem Based on Discrete Particle Swarm Optimization , 2005 .

[6]  Guy Desaulniers,et al.  Chapter 2 Public Transit , 2007, Transportation.

[7]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Dušan Teodorović,et al.  Swarm intelligence systems for transportation engineering: Principles and applications , 2008 .

[9]  Yousef Shafahi,et al.  Application of a new rapid transit network design model to bus rapid transit network design: case study Isfahan metropolitan area , 2013 .

[10]  Ángel Marín.  An extension to rapid transit network design problem , 2007 .

[11]  Avishai Ceder,et al.  Public Transit Planning and Operation , 2007 .

[12]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[13]  Laureano F. Escudero,et al.  An approach for solving a modification of the extended rapid transit network design problem , 2009 .

[14]  Michael N. Vrahatis,et al.  Particle Swarm Optimization and Intelligence: Advances and Applications , 2010 .

[15]  J. Mark Bishop.  Stochastic diffusion search , 2007, Scholarpedia.

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[18]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[19]  Shahab A Kermanshahi.  Rapid Transit Network Design: A New Model and Solution Algorithm , 2010 .

[20]  Matthew G. Karlaftis,et al.  Transit Route Network Design Problem: Review , 2009 .

[21]  Hossain Poorzahedy,et al.  Application of particle swarm optimization to transportation network design problem , 2011 .