Predicting the Automated Vehicle Penetration using Mamdani Fuzzy Inference System for the city of Istanbul, Türkiye

The advancement of automated vehicles is set to reshape transportation systems and urban development patterns, making it crucial to understand their future penetration. This study aims to predict the types and extent of automated vehicle (AV), shared automated vehicle (SAV), and shared automated electric vehicle (SAEV) penetration in Istanbul using a predictive fuzzy-based model. The Mamdani fuzzy inference system, incorporating five main criteria and nine sub-criteria, was applied to handle imprecise information from expert opinions and to simulate various planning scenarios from 2020 to 2060. The importance and motivation of this study lie in the need to anticipate and plan for the transformative impact of self-driving vehicles on urban transportation systems, ensuring that cities are prepared for the technological, infrastructural, and policy changes required for their integration. The research addresses the challenge of predicting how different types of self-driving vehicles will penetrate urban areas, helping to guide future planning and infrastructure development in Istanbul. The model results indicate that self-driving vehicles are expected to be concentrated in central business districts (CBDs) such as Kadıkoy, Beşiktaş, Sarıyer, and Üsküdar, driven by advanced transportation options and technological opportunities. Suburban residents, in contrast, are projected to prefer autonomous public transit over private AVs. Based on the predicted SAV and SAEV demands, there is a need to increase pick-up/drop-off points and charging stations, especially in districts like Çatalca and Silivri on the European side and Şile and Tuzla on the Asian side. Addressing parking challenges in the CBDs will also be necessary. The study’s findings offer valuable insights for transportation planners and decision-makers, highlighting the importance of enhancing electronic infrastructure, V2X communication, and reducing costs to promote autonomous vehicle adoption. Overall, this research contributes to future urban and transportation planning, helping predict driverless vehicle integration up to 2060 and guiding the design of essential infrastructure for Istanbul’s evolving mobility landscape.