The present work approaches intelligent traffic evaluation and congestion detection using sound sensors and machine learning. For this, two important problems are addressed: traffic condition assessment from audio data, and analysis of audio under uncontrolled environments. By modeling the traffic parameters and the sound generation from passing vehicles and using the produced audio as a source of data for learning the traffic audio patterns, we provide a solution that copes with the time, the cost and the constraints inherent to the activity of traffic monitoring. External noise sources were introduced to produce more realistic acoustic scenes and to verify the robustness of the methods presented. Audio-based monitoring becomes a simple and low-cost option, comparing to other methods based on detector loops, or GPS, and as good as camera-based solutions, without some of the common problems of image-based monitoring, such as occlusions and light conditions. The approach is evaluated with data from audio analysis of traffic registered in locations around the city of São Jose dos Campos, Brazil, and audio files from places around the world, downloaded from YouTube. Its validation shows the feasibility of traffic automatic audio monitoring as well as using machine learning algorithms to recognize audio patterns under noisy environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Audio-Based Machine Learning Model for Traffic Congestion Detection




    Publication date :

    2021-11-01


    Size :

    2060270 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    TRAFFIC CONGESTION DETECTION DEVICE, TRAFFIC CONGESTION DETECTION SYSTEM, AND TRAFFIC CONGESTION DETECTION METHOD

    ESHITA NAOHIKO / MORIGUCHI TAKUO / TOKUUME SHINYA et al. | European Patent Office | 2022

    Free access

    Machine Learning Solutions to Vehicular Traffic Congestion

    Chhatpar, Pavan / Doolani, Nimesh / Shahani, Sumeet et al. | IEEE | 2018


    Machine Learning Based Traffic Congestion and Accident Prevention Analysis

    Sofia, A. Sathya / Selvi, C. P. Thamil / Suganya, S. et al. | Springer Verlag | 2024


    Traffic intersection congestion prediction method based on machine learning

    XU WENBO / LIAO ZHIZHOU / HU SIQUAN et al. | European Patent Office | 2020

    Free access

    A Traffic Congestion Forecasting Model using CMTF and Machine Learning

    Chowdhury, Md. Mohiuddin / Hasan, Mahmudul / Safait, Saimoom et al. | IEEE | 2018