An extensible framework for urban mobility digital shadows

Abstract

Urban mobility systems require continuous monitoring and optimization to address increasing complexity and sustainability demands. This paper introduces UM-DS (Urban Mobility–Digital Shadow), a modular framework that integrates heterogeneous real-time and historical data, such as traffic intensity, incidents, weather, and public transport, into a unified model of urban mobility. UM-DS enables visualization, simulation, and analysis through an extensible dashboard connected to the SUMO simulation engine. Validated in the city of Malaga, the framework supports real-time exploration and temporal playback of traffic conditions, facilitating the development of AI-based forecasting models and multi-agent simulations. Its flexible, city-agnostic architecture allows adaptation to diverse urban contexts, bridging the gap between raw data and intelligent decision making. UM-DS provides a replicable foundation for next-generation smart-city mobility systems, fostering informed, data-driven strategies for sustainable urban transport.