The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real- Time Traffic Prediction And Optimization using Machine Learning




    Publication date :

    2025-02-05


    Size :

    1275207 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Real Time Traffic Management Using Machine Learning

    Tiwari, Jyoti / Deshmukh, Ankita / Godepure, Gayatri et al. | IEEE | 2020


    Autonomous Vehicle Navigation Systems: Machine Learning for Real-Time Traffic Prediction

    Praveen, RVS / Hundekari, Sheela / Parida, Prasanta et al. | IEEE | 2025


    Traffic Prediction Using Machine Learning

    Deekshetha, H. R. / Shreyas Madhav, A. V. / Tyagi, Amit Kumar | Springer Verlag | 2022



    Traffic Prediction for Real Time Traffic Management

    Olsson, K. | British Library Conference Proceedings | 1995