Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

Abstract

This work utilizes the transit network, which aims to combine the genetic algorithm for analyzing the theory and traffic scheduling based on the traditional methodology. The dynamic methodology is used to schedule the model of transit system, which aims to optimize the demand in the transit network. This model illustrates the methodology of the genetic based transit network (GATN) algorithm to enhance the primary challenges in the transit network. The proposed methodology provides to be significant, with minimizing the objective model of around 27.2%. The model significantly managed to lower the total routes available in the transit network and all travelers related to the time and the transit trip from the initial stage. The significant system obtained using the optimization methodology has 180 routes, 110 less than the initial network, which has a variation by different transit network. This final transmission has been minimized to 33.6% by the proposed methodology in the transit network length and 4.1% reduction in the transfer average. The transition obtained from the multi-level objective function to unique optimization that considers the weighted function proved to be effective