Intelligent Traffic Management System Using Machine Learning and Traffic Signal Control Algorithms for Optimizing Vehicular Flow in Lima

Toribio J.; Huarcaya F.; Huarcaya N. · Communications in Computer and Information Science (2025) · DOI: 10.1007/978-3-031-89760-3_4

Resumen

A 2023 TomTom study identifies Lima as the most congested city in Latin America and the fifth worldwide, causing significant impacts such as increased stress, long travel times, and lower productivity. Despite various approaches, none fully address the unique challenges of developing cities like Lima. To address this, the proposed system uses the Max Pressure (MP) algorithm for traffic signal control and a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for traffic prediction. The MP algorithm dynamically adjusts signal timing to optimize traffic flow, while the Bi-LSTM predicts future traffic patterns. Validated through simulations on Javier Prado Avenue, the system achieved a 13.8% increase in vehicle throughput and an 18% reduction in travel times. These findings highlight its potential to enhance urban traffic management. The developed web app offers a practical tool, bridging research and real-world application to address Lima's traffic congestion effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Instituciones: Universidad Peruana de Ciencias Aplicadas