Recent research:
The researcher conducts systematic reviews and empirical studies to advance resilient and sustainable mobility in urban and national contexts, focusing on connected and autonomous vehicle (CAV/AV) effectiveness, governance, and safety. Using PRISMA-guided literature syntheses, the work identifies how CAVs and AVs can enhance network robustness, guide backcasting for future city visions, and shape policy and stakeholder engagement toward sustainable, data-driven transportation systems. Several studies apply computer vision and machine learning to improve road safety and AV perception—e.g., recognizing traffic officers and interpreting officer gestures—while also examining driver behavior and risk factors in Ecuador. Methodologically, the portfolio emphasizes rigorous data collection (including datasets from web crawling and media sources), quantitative modeling (e.g., Poisson regressions, FEM in safety analysis), and evaluation of detection performance (F1 and accuracy). Collectively, the research contributes practical insights for policy, infrastructure design, and safety standards to support resilient, ethically governed, and environmentally conscious mobility systems, with a specific emphasis on Latin American contexts, particularly Ecuador.
Key Topics:

• Transportation, Mobility, Vehicles & Emissions
• Artificial Intelligence, Machine Learning & Data Science
• Computer Vision, Medical Imaging & Signal Processing
• Research Methods, Measurement, Bibliometrics & Academic Practice
• Governance, Law, Policy, Ethics & Regulation