Recent research:
The body of work presents a cohesive program to understand and improve urban mobility in Cuenca, Ecuador through a multidisciplinary lens that blends transportation engineering, geospatial analysis, and digital city technologies. Across studies, the author collects rich sensor- and location-based data to classify transport modes, quantify energy use, emissions, and driving styles, and to explore how mobility patterns reflect urban form, functionality, and environmental impacts. Methodologically, the research integrates machine learning (decision trees, SVM, KNN, neural networks), fuzzy logic diagnostics, real-driving emissions concepts, DOE-based engine parameter tuning, non-intrusive in-vehicle measurements (OBD-II data, gas analyzers, opacimeters), and GIS/remote sensing to map and model origin–destination flows and emissions inventories. The findings consistently demonstrate that data-driven classification and mobility modeling can support informed urban planning, targeted public transit optimization, and safer, more efficient driving practices, with particular emphasis on Cuenca’s intermediate-city context and its potential to reduce emissions and transportation costs while enhancing quality of life. The work also probes the feasibility of electric mobility corridors, energy demand in hybrid and electric powertrains, and the alignment of COPERT/EURO6 standards with local driving realities to improve emissions inventories. Together, these publications contribute a coherent framework for smarter, greener urban mobility through data-centric analysis, real-world testing, and actionable planning tools.

Key Topics:

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