Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea

Internationally, there are heightened demands for efficient public transportation systems due to high population growth rates in urban areas and their associated increased trip demands within and across city boundaries. An ideal and sustainable public transportation system should satisfy its passengers while minimizing operation costs that are often associated with energy consumptions. One such cost-effective approach is establishing an integrated public transit system. A transit system generally includes a set of bus routes and rail lines connected by transfer stations. The main objective of this research is to propose a sustainable and integrated transit establishment model to design an optimal bus transit system in combination with an existing railway system dealing with both fixed and variable demands while satisfying multiple objectives. Moreover, this paper finds an optimum set of transit routes that corresponds to chosen tradeoffs between user cost, operator cost and, notably, unsatisfied demand cost. Optimal transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms including the genetic algorithm and the non-dominated sorting genetic algorithm II (NSGA-II). The study area is chosen as Daejeon City, South Korea for its strategic location. Compared with existing transit networks, the proposed approach shows significant improvements in terms of costs. In addition, the proposed approach can provide an efficient methodology for finding alternative alignments of existing transit systems for decision makers.