This chapter focuses on the transforming effect of machine learning (ML) algorithms in traffic control systems through the use of advanced technologies. Given the worsening situation in large‐sized towns and cities where there is traffic congestion, rates of accidents are doing rise and crises for environment increase, new approach is very necessary. Machine learning provide possible solutions that can deal with this problem through provide more accurate traffic flow predictions, handle real‐time incident management as well as implement dynamic route optimizations. This chapter delves in the structural analysis of different machine learning algorithms specifically intended for traffic data analysis, goes further to practically apply the algorithms he discusses different case studies drawn from cities that were able to integrate these technologies and fully use them. Information disclosure impends the full potential of ML in traffic management and gives rise to data quality, privacy, and real‐time processing challenges. The next generation of the traffic systems, mobility aided by cutting‐edge ML and, a home to new technologies like the IoT and the autonomous vehicles, is presented to provide a holistic view of how an intelligent transportation system can be improvised for the future.


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    Titel :

    Predicting the Flow with Machine Learning Algorithms for Advanced Traffic Management


    Beteiligte:
    Gatti, Rathishchandra R. (Herausgeber:in) / Singh, Chandra (Herausgeber:in) / Dankan Gowda, V. (Autor:in) / Suraskar, Rupali (Autor:in) / Rani, Ridhi (Autor:in) / Prasad, K.D.V. (Autor:in) / Srinivas, Ved (Autor:in)


    Erscheinungsdatum :

    07.08.2025


    Format / Umfang :

    23 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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