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.


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    Titel :

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


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Botto-Tobar, Miguel (Herausgeber:in) / Lema Moreta, Lohana (Herausgeber:in) / Zambrano Vizuete, Marcelo (Herausgeber:in) / Montes León, Sergio (Herausgeber:in) / Torres-Carrion, Pablo (Herausgeber:in) / Durakovic, Benjamin (Herausgeber:in) / Toribio, Julio (Autor:in) / Huarcaya, Frank (Autor:in) / Huarcaya, Nelly (Autor:in)

    Kongress:

    International Conference on Applied Technologies ; 2024 ; Samborondon, Ecuador November 21, 2024 - November 23, 2024



    Erscheinungsdatum :

    14.05.2025


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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