Çok amaçlı hızlı ulaşım ağı tasarımı

Urban rapid transit network design consists of the location of train alignments and stations in an urban traffic context. There are so many criteria that may be taken into account by many decision makers that are involved in design and construction of these alignments because transit networks, like other types of transportation networks, have social and economical affects on the inhabitants of any city or region.Since the importance of sustainability in urban planning increases in the last decades, authorities pay attention to create more environment-friendly solutions. Air pollution in many cities in the world, especially in highly populated regions, is attributed to the emissions of vehicles. An important mean to reduce these side effects is to enhance public transportation. This study focuses on the design of rapid transit networks because public surface transportation with or without privileged lanes also release harmful gases most of the time. However, it should be noted that the most significant aspect of this type of networks is their required investment costs which are relatively higher than any other means. Moreover, the higher the number of stations involved in the alignment, the more costly the construction will be. In this study, the design of a single transit line with no predetermined origin and destination is investigated while considering the existing road network. The problem is formulated as a bi-level multi-objective optimization problem where the minimization of line investment and vehicle emissions are considered as two separate objectives at the upper level, and user traffic behavior is considered at the lower level. The model reflects the mode choice of the users which are assumed to act rationally by selecting the shortest time path to their destination. The well known multi-objective genetic algorithm NSGA-II is adapted so as to find non-dominated solutions of the problem, and a variant of Frank-Wolfe algorithm is used to solve the lower level traffic assignment problem. The proposed algorithm is applied on a benchmark problem existing in the literature and important insights are provided.