Recent research:
Across the publications, the researcher advances understanding of urban mobility, transport systems, and their environmental and societal impacts through data-driven modeling, machine learning, and sensor-enabled analyses. The work synthesizes real-world driving data (OBD II, GPS, PIDs) with advanced statistical methods (Gamma regression, linear models) and AI techniques (KNN, SVM, decision trees, ANN) to (i) model fuel efficiency and energy consumption in LDVs and EVs under varied topographies and driving styles; (ii) classify driving maneuvers, driving modes (ecological, RDE, aggressive), and transportation modes to support urban planning and policy; (iii) connect vehicle emissions to construction characteristics and gear use, and (iv) propose replicable frameworks for FE estimation, gear-shift classification, and driver assistance integration. The impact lies in delivering robust predictive models with high accuracy (e.g., R2 ~0.9, MAE low), actionable insights for fuel efficiency, emissions reduction, and smart city planning through digital twins, sensors, and data science. The work consistently emphasizes real-world applicability in Latin American urban contexts (Cuenca, Quito) and contributes to sustainable mobility, energy efficiency, and informed urban governance.

Key Topics:

• Transportation, Mobility, Vehicles & Emissions
• Artificial Intelligence, Machine Learning & Data Science
• Energy Storage, Batteries & Electric Mobility
• Sensors, Instrumentation & Measurement Systems
• Geospatial Analysis, Remote Sensing & GIS
• Urban Digital Transformation, Smart Cities & Digital Twins