Metropolitano Plus: A Machine Learning-Based Mobile Application for Predicting Bus Arrival Times in the Corredor Metropolitano of Lima

Traverso D.; Pacheco G.; Wong-Durand S.; Castaneda P.; Onate-Andino A. · Engineering, Technology and Applied Science Research (2026) · DOI: 10.48084/etasr.14855

Resumen

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. © (2026), (Dr D. Pylarinos). All rights reserved.

Instituciones: Universidad Peruana de Ciencias Aplicadas; Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas