Bir sürdürülebilir toplu taşıma sefer sıklığı belirleme yaklaşımı ve İstanbul uygulaması

As a result of economic growth, the trend of urbanization has continued since the industrial revolution, when the increasing opportunities for jobs, education, housing, and reduced commuting and transportation cost attract new immigrants from rural areas. However urbanization is also viewed as a negative trend because of its side effects, such as traffic congestion, environmental impacts, segregation, as well as other inequity issues. Increasing environmental concerns as well as economic and social impacts of transportation in communities necessitate the incorporation of sustainability into the planning process. Along this line, we develop a sustainable transit assignment model in this study. The model is formulated as a bi-level optimization model with two objectives: minimizing the average passenger travel time and minimizing the total carbon dioxide emitted from the bus fleet. With these two objectives, we identify the optimum line frequencies at the upper level while considering the fleet size and the minimum service constraints. At the lower level, we model the transit route choice of the passengers with the objective to minimize the total in-vehicle and station waiting times given passengers demand and existing lines. A genetic algorithm that is known to efficiently solve multi-objective programming problems is adapted to solve the problem. The efficiency of the model and the associated solution method is validated through a case study undertaken in Istanbul. The bus network of Istanbul is one of the largest networks across Europe with 593 lines serving 39 distinct zones and 3 million passengers daily in 2012. The sustainable solution is identified among the Pareto solutions obtained by the solution method, and it is shown that network emission could be reduced more than fifty percent while having average passenger travel time shorter than the actual situation by altering line frequencies.Key Words: Sustainable Public Transportation, Transit Frequency Setting, Bilevel Programming, Multiobjective Programming, Genetic Algorithm