T-RAPPI: A Machine Learning Model for the Corredor Metropolitano

The public transportation system in Lima, Peru, faces significant challenges, including bus shortages, long queues, and severe traffic congestion, which diminish service quality. These issues arise from a lack of modern management tools capable of efficiently handling the Metropolitano bus system. This paper introduces T-RAPPI, a predictive model based on Random Forest, developed to estimate bus arrival times at Metropolitano stations. Using historical data on bus arrivals and operational parameters, the model achieved exceptional accuracy, with an R² score of 0.9998 and a MAPE of 0.0554%, demonstrating its robustness and ability to minimize prediction errors. The implementation of T-RAPPI represents a substantial improvement over existing systems, providing operators with data-driven insights to optimize route planning and bus allocation. Additionally, the model's integration into the mobile application Metropolitano + enhances the commuting experience by offering users real-time bus arrival predictions, reducing uncertainty and wait times. Future extensions of this work could include incorporating real-time traffic and weather data to further enhance prediction accuracy and expanding the model to other transit systems in Lima and beyond.