Transit Route Network Design Using Parallel Genetic Algorithm

Abstract

A transit route network design (TRND) problem for urban bus operation involves the determination of a set of transit routes and the associated frequencies that achieve the desired objective. This can be formulated as an optimization problem of minimizing the total system cost, which is the sum of the operating cost and the generalized travel cost. A review of previous approaches to solve this problem reveals the deficiency of conventional optimization techniques and the suitability of genetic algorithm (GA) based models to handle such combinatorial optimization problems. Since GAs are computationally intensive optimization techniques, their application to large and complex problems is limited. The computational performance of a GA model can be improved by exploiting its inherent parallel nature. Accordingly, two parallel genetic algorithm (PGA) models are proposed in this study. The first is a global parallel virtual machine (PVM) parallel GA model where the fitness evaluation is done concurrently in a parallel processing environment using PVM libraries. The second is a global message passing interface (MPI) parallel GA model where an MPI environment substitutes for the PVM libraries. An existing GA model for TRND for a large city is used as a case study. These models are tested for computation time, speedup, and efficiency. From the study, it is observed that the global PVM model performed better than the other model.