A public transport network design using a hidden Markov model and an optimization algorithm

Abstract

Transportation Network Design Problem (TNDP) includes making the right choices possible when deciding a collection of design criteria to develop a current transportation network in response to rising traffic demand. Traffic congestion, higher maintenance and fuel prices, delays, accidents, and air emissions stem from the general rise in flow volume. Because of the NP-hard nature of this problem, a hidden Markov model and an Equilibrium Optimizer (EO) are employed in this paper to solve it. Each particle (solution) behaves as a search agent in EO, with its position. To reach the equilibrium condition, the search agents change their focus at random regarding the best-so-far approaches, including equilibrium candidates. A well-defined "generation rate" concept has been shown to elevate EO's capacity in avoiding local minima. This article provides a new method to lower the feasible travel time and the public travel cost using the hidden Markov model and EO algorithm. The suggested method's performance was compared to the performance of other algorithms on a test network. The related numerical outcomes show that it is more effective.