Optimizing Smart City Street Design with Interval-Fuzzy Multi-Criteria Decision Making and Game Theory for Autonomous Vehicles and Cyclists

Abstract

Highlights: What are the main findings? Safety is the most critical factor in designing urban streets that integrate cyclists and autonomous vehicles (AVs); Green infrastructure and smart technology adoption are the optimal integration strategies. What are the implications of the main findings? These strategies foster a balanced coexistence of cyclists and AVs, leading to a more efficient transport system and a more sustainable urban environment in the driverless era. This research provides valuable guidance for urban planners and decision makers on the implementation of AVs on our streets, while advocating for sustainable and active mobility. Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology.