Metropolitano Plus: A Machine Learning-Based Mobile Application for Predicting Bus Arrival Times in the Corredor Metropolitano of Lima
Received: 17 September 2025 | Revised: 10 November 2025, 4 December 2025, 15 December 2025, 13 January 2026, and 18 January 2026 | Accepted: 21 January 2026 | Online: 4 April 2026
Corresponding author: Sandra Wong-Durand
Abstract
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.
Keywords:
machine learning, random forest, bus arrival prediction, mobile application, intelligent transportation systems, public transit in Latin AmericaDownloads
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Copyright (c) 2026 Deneb Traverso, Gonzalo Pacheco, Sandra Wong-Durand, Pedro Castaneda, Alejandra Onate-Andino
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