Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies.

This study evaluates the feasibility of electrifying the urban public transport system in Cuenca, Ecuador, through an approach that combines real data acquisition, dynamic-energy modeling, and optimization. Position (latitude, longitude, altitude) and speed data were collected across 35 urban routes to calculate longitudinal forces, energy consumption, and the state of charge of a reference electric bus. Based on this analysis, the avoided $\text{CO}_{2}$ emissions resulting from replacing diesel buses were estimated. To optimize the allocation of electric units, the Football Optimization Algorithm was implemented, formulated as a maximization problem of avoided $\text{CO}_{2}$ under capital investment and fleet-size constraints. Results show that the electrification of 150 buses, with an investment of USD 60 million, can reduce approximately 467 kg of $\text{CO}_{2}$ per day, prioritizing high-consumption routes such as lines 16, 21, and 27. The FOA demonstrated fast and stable convergence in fewer than 100 iterations, strategically distributing units to maximize environmental impact and cost-effectiveness. The proposed methodology provides a replicable framework for other Latin American cities with similar geographic and operational conditions, supporting decision-making for sustainable urban mobility.