Traffic gridlock is a significant issue in urban centers with growing populations. The increased vehicle congestion has severe consequences, including elevated air and noise pollution levels, higher fuel consumption, and increased stress among commuters. These conditions disrupt daily routines and adversely affect economic productivity and public health. Many cities rely on outdated traffic management systems that operate on fixed schedules and lack real-time adaptability, which worsens congestion. As vehicle numbers surge, replacing these conventional systems with more advanced solutions becomes crucial. The proposed approach emphasizes the integration of machine learning algorithms for real-time vehicle detection and traffic analysis. By comparing various machine learning models, this research aims to identify the most effective algorithm for managing urban traffic, enabling a responsive system that adjusts to live traffic conditions. This method offers a promising solution to alleviate gridlock, address community needs, and reduce the environmental and social costs associated with traffic congestion


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Traffic Optimization for Urban Traffic Congestion


    Contributors:


    Publication date :

    2025-02-08


    Size :

    1087048 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Urban traffic congestion

    Orchard, D.F. | Engineering Index Backfile | 1959


    Urban traffic-congestion problem

    MacDonald, H.A. | Engineering Index Backfile | 1946


    Managing Urban Traffic Congestion

    OECD / European Conference of Ministers of Transport | SLUB | 2007


    Alleviating urban traffic congestion

    Arnott, Richard / Rave, Tilmann / Schöb, Ronnie et al. | SLUB | 2005


    Alleviating urban traffic congestion

    Arnott, Richard / Rave, Tilmann / Schöb, Ronnie et al. | TIBKAT | 2005