Urban mobility and transportation in circular cities: Integrating blockchain–AI digital solutions

Source abstract

Urban mobility systems face mounting challenges, including congestion, air pollution, modal fragmentation, infrastructure deficits, and fossil fuel dependency, which hinder the transition to circular city models. This study systematically examines how blockchain and artificial intelligence (AI) can address six validated transportation and mobility challenges within the Circular City Actions Framework (CCAF). Employing a two-stage methodology, we first identify and prioritize key challenges through expert surveys and the Relative Importance Index (RII). Subsequently, a two-round Delphi process stabilizes expert judgments on the relevance of ten blockchain and AI properties for each challenge, followed by Bayesian Best–Worst Method (BBWM) analysis to derive probabilistic importance weights. Results reveal that AI capabilities, such as predictive analytics, real-time monitoring, multimodal optimization, and demand forecasting, are most effective in addressing operational challenges such as congestion, modal integration, and public transport efficiency. Conversely, blockchain properties, including transparent data sharing, smart contracts, emissions tracking, and carbon-credit systems, are prioritized to address systemic and environmental challenges, notably air pollution and the reduction of fossil fuel use. Cross-cutting enablers, such as transparent data sharing and real-time monitoring, consistently rank highly across multiple challenges, underscoring their foundational role in digital transformation. The study proposes a managerial framework for technology selection, emphasizing the synergistic deployment of blockchain and AI to advance sustainable, resilient, and circular urban mobility. Findings offer actionable guidance for policymakers and practitioners, highlighting the need for tailored digital interventions aligned with specific urban mobility challenges and circular city objectives.