Parallel genetic algorithm in bus route headway optimization

In this paper, a model for optimizing bus route headway is presented in a given network configuration and demand matrix, which aims to find an acceptable balance between passenger costs and operator costs, namely the maximization of service quality and the minimization of operational costs. An integrated approach is also proposed in the paper to determine the relative weights between passenger costs and operator costs. A parallel genetic algorithm (PGA), in which a coarse-grained strategy and a local search algorithm based on Tabu search are applied to improve the performance of genetic algorithm, is developed to solve the headway optimization model. Data collected in Dalian City, China, is used to verify the feasibility of the model and the algorithm. Results show that the reasonable resource assessment can increase the benefits of transit system.