Designing Urban Transit Network using Memetic Algorithm

Abstract

Urban transit network design (UTND) problem represents a challenge in designing routes, with a trade-off between serving passengers and operators benefits. In this study, a Memetic Algorithm (MA) is proposed to solve the UTND problem. The algorithm uses the hill climbing local search (HCLS) algorithm as an additional operator for Genetic Algorithm (GA) to improve routes construction during the global search. The proposed method consists of two phases. In the first phase, a set of solutions (transit network designs) is generated as an initial population for MA, where each solution consists of a set of routes. The predefined set of solutions satisfies the constraints such as route length or number of routes, and requirements like lack of loops, and that all nodes are covered by at least one route. In the second phase, the suggested Memetic Algorithm (MA) is used to generate all possible solutions from the predefined set. The MA tries to find the best structured solution that represents the flawless transit network. The proposed MA is applied on the widely examined benchmark problems: Mandl and Mumford networks. The experiment results show that the suggested MA provides significant improvements in terms of the direct trip percentage and average travel time compared to the previous studies.