This research paper addresses the escalating challenges of urban traffic congestion and diminished driving safety resulting from the burgeoning automobile population. The study formulates an innovative framework for an Enhanced Traffic Management Model (ETMM) leveraging the convergence of Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. Through in-depth interviews with a diverse panel of experts and comprehensive data analysis, the framework integrates real-time and historical data streams for accurate traffic prediction, congestion estimation, and dynamic signal optimization. The findings reveal that the proposed ETMM substantially improves traffic flow, minimizes travel time, and enhances urban mobility. Additionally, the research demonstrates the viability of AI-driven approaches for traffic management, underscored by successful model deployment and validation. The future scope lies in refining the framework with continuous data updates, incorporating user-centric insights, and expanding its applications to create safer, more efficient urban transportation systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Smart Traffic Control and Prediction Model Empowered with 5G Technology, Artificial Intelligence and Machine Learning


    Contributors:


    Publication date :

    2023-11-02


    Size :

    513975 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Artificial Intelligence Empowered Models for UAV Communications

    Pradhan, Nilanjana / Sille, Roohi / Sagar, Shrddha | Springer Verlag | 2022


    Artificial Intelligence-Based Smart Traffic Control System

    Tiwari, Amit Kumar / Pandey, Raghvendra Kumar / Singh, Saharsh et al. | Springer Verlag | 2024


    Smart Traffic Signal Control System Using Artificial Intelligence

    Kumari, G. R. P. / Jahnavi, M. / Harika, M. et al. | Springer Verlag | 2023


    AN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING BASED TRAFFIC CONTROL SYSTEM

    MARAM BALAJEE / SRINADH V / ESWARI DUTTA SAI et al. | European Patent Office | 2020

    Free access