Metropolitano Plus: A Machine Learning-Based Mobile Application for Predicting Bus Arrival Times in the Corredor Metropolitano of Lima - Traverso, Deneb; Pacheco, Gonzalo; Wong-Durand, Sandra; Castaneda, Pedro; Onate-Andino, Alejandra (Engineering, Technology & Applied Science Research, 2026)
This study aimed to enhance the efficiency and reliability of Lima’s Metropolitan Bus system by applying machine learning to predict bus arrival times and support data-driven operational management. T-RAPPI is a predictive model based on the Random Forest algorithm, trained with historical operational data from the Corredor Metropolitano. The model achieved high predictive accuracy (R² = 0.9998, MAE = 0.0062 min), demonstrating its ability to reproduce real operational patterns. These predictions were integrated into the Metropolitano Plus mobile application, developed with Flutter and Firebase, which provides real-time bus arrival forecasts, station occupancy visualization, and trip evaluation features. By improving information reliability and reducing passenger waiting times, the proposed solution enhances both user experience and operational efficiency. A user validation survey based on the ISO/IEC 25010 quality standard reported satisfaction levels above 88% across all quality dimensions. Future work will focus on incorporating real-time traffic data and expanding the system to other public transport networks in Lima and similar urban contexts in Latin America.