Bus type selection with fuzzy approach for public transportation

Buran B., Erçek M.

Systems and Soft Computing, vol.5, 2023 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 5
  • Publication Date: 2023
  • Doi Number: 10.1016/j.sasc.2023.200055
  • Journal Name: Systems and Soft Computing
  • Journal Indexes: Scopus
  • Keywords: Bus type selection, Fuzzy approach, Green transportation, Multi-criteria decision-making method, Public transportation
  • Istanbul Technical University Affiliated: Yes


There is tightening political pressure on cities to publish zero-emission strategies to decrease greenhouse emissions. To achieve these targets, decreasing public transportation emissions has become critical. There are different types of green transportation such as electric, compressed natural gas, hydrogen, biofuel, etc. This study develops a model regarding a holistic view that not only focuses on economic and operational conditions but also on business and strategy to decide the best bus type for public transportation for Istanbul metropolitan area. To handle the vagueness and complexity of the problem, the Spherical Fuzzy AHP method is applied to the model, as the method can handle three membership functions, which are membership, non-membership, and hesitancy. Also, to compare the results a classical AHP is performed with the same data. Results show that the diesel bus type stands out as the best option for both methods regarding the chosen criteria for Istanbul. The others varied with respect to methods. This study sheds light on results that could vary regarding the country's context such as economic, social, and political factors. Although there are lots of studies on vehicle selection problems by applying multi-criteria decision-making methods, many of them have not utilized Spherical Fuzzy Set. To test the proposed model, a real case study is conducted. From this point, this study will not only contribute to the literature but also inform decision-makers and practitioners in terms of prioritizing their selection criteria.