SMART-CROWD: A System Architecture for Intelligent Assessment of Crowdsourcing Maturity in Urban Mobility Governance

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility.