@revista_internacional{915,
  keywords = {Social robots, Urban transportation, Human-robot interaction, Fuzzy sets, multi-criteria decision making, Trigonometric operators},
  author = {Muhammet Deveci and Dragan Pamucar and Ilgin Gokasar and Bilal Bahaa Zaidan and Luis Martínez and Witold Pedrycz},
  title = {Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model},
  abstract = {Current trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decision-making tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights; ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWAS-WASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation.},
  year = {2023},
  journal = {Technological Forecasting and Social Change},
  volume = {194},
  pages = {122743},
  issn = {0040-1625},
  url = {https://www.sciencedirect.com/science/article/pii/S0040162523004286},
  doi = {https://doi.org/10.1016/j.techfore.2023.122743},
}