Jorge de Jesus Lozoya Santos

Urban traffic congestion poses increasing challenges for intelligent mobility systems, particularly as robotic platforms require reliable infrastructure-aware information for decision-making. This paper presents a crossdomain digital twin framework that integrates empirically grounded traffic demand modeling, multi-agent simulation, adaptive signal control, and synchronized 3D visualization within a unified architecture. Signalized intersections are modeled as interacting vehicle and traffic light agents operating over a graph-based network. Infrastructure-level control is formulated as a Markov Decision Process (MDP), enabling adaptive and interpretable signal phase allocation based on observed queue states. Simulation outputs-including congestion dynamics, delay metrics, and signal evolution-are exported and synchronized with a Unity-based environment, forming a spatially and temporally consistent digital twin. Comparative evaluation against a fixed-time baseline demonstrates reductions in waiting time and queue length, together with increased throughput under realistic demand conditions. The framework further establishes a structured interface through which robotic agents, such as neighborhood electric vehicles (NEVs), can consume predictive infrastructure information to support navigation decisions. The results demonstrate that integrating adaptive multi-agent control with digital twin synchronization provides measurable traffic-level improvements while enabling scalable cross-domain coordination between infrastructure intelligence and robotic autonomy.

Rapid urbanization and climate change pressure cities to integrate sustainability, digitalization, and circular economy principles. Yet most existing approaches treat these agendas separately, leaving gaps in how urban infrastructures, governance, and data systems can jointly support circular transformations. This paper addresses this challenge by proposing and validating a Sustainable, Smart, and Circular City (SSCC) architecture that operationalizes the waste–energy–information nexus. The architecture is structured into seven interconnected layers—Physical, Digital, Analytical, Participatory Governance, Data Strategy, Innovation Management, and Assessment—and is tested through two integrated experiments in the Fenicia Urban Living Lab, Bogotá: (i) an AI- and drone-based system for waste detection and community reporting and (ii) a solar-powered IoT urban garden for environmental monitoring. These experiments demonstrate how digital twins, participatory governance, and multi-actor collaboration can activate circular strategies while enabling evaluation against international standards (ISO 37106, U4SSC, LEED). The results confirm that the SSCC model can transform siloed services into integrated, circular functions that enhance quality of life, productivity, and ICT-based sustainability. The originality of this study lies in validating an SSCC architecture that incorporates the waste–energy–information nexus across seven layers and demonstrates, through Urban Living Lab experimentation, how such an architecture can guide the transition from Smart Sustainable Cities to Circular Cities.

Water is an essential resource for life and is also a necessary resource for the sustainable economic competitiveness of any country. In recent decades, climate change, economic development, and rising population have led to water scarcity in certain regions. In response, new technologies and water management techniques have been researched and developed, which are now incorporated into the concept of smart cities. These innovations, called smart water systems, aim to enhance water management by monitoring consumption, quality, reservoir levels, leaks, and asset conditions, and optimizing water processes to maximize water system resilience. The first systems were based on smart meters and have advanced to so-called digital twins for water systems. This review aims to present a comprehensive review of smart water system developments, the geographic distribution of the works, their technological readiness level, and their implementation challenges.