The efficient management of road traffic is crucial for enhancing transportation routing and improving overall traffic flow. However, the conventional methods can not accurately analyze real-time traffic data and do not provide valuable insights for effective transportation routing decisions. So, this work proposes a deep learning-based approach for road traffic condition monitoring networks (RTCM-Net) for various illumination conditions. Initially, the fuzzy block-based histogram equalization (FBHE) method enhances the colour properties of the input image, which improves the low-light conditions, haze removal, illumination condition balancing, and colour balancing. The proposed approach leverages deep learning techniques, specifically convolutional time domain neural networks (CTDNN), to learn and extract meaningful features from road traffic data. By training the CTDNN model on a large-scale dataset comprising historical traffic patterns, the system can effectively capture complex traffic conditions and identify anomalies or congestion in real time. Finally, the RTCM-Net is capable of classifying the high, low, dense traffic, fire attack, and accident classes from the input images. The proposed RTCM-Net achieved high accuracy at 99.51%, sensitivity at 98.55%, specificity at 98.92%, F-measure at 99.98%, precision at 99.42%, Matthews Correlation Coefficient (MCC) at 99.72%, Dice as 98.62%, and Jaccard as 99.48% scores, indicating its effectiveness in classifying and monitoring road traffic conditions, which are higher than traditional approaches.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep learning based condition monitoring of road traffic for enhanced transportation routing


    Weitere Titelangaben:

    J Transp Secur


    Beteiligte:
    Srinivasarao, Goda (Autor:in) / Penchaliah, U. (Autor:in) / Devadasu, G. (Autor:in) / Vinesh, G. (Autor:in) / Siva Varma, P. Bharath (Autor:in) / Kallur, Sudhakar (Autor:in) / Kumar, Pala Mahesh (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Customized CNN-Based Condition Monitoring of Road Traffic for Intelligent Transportation Routing

    Penchalaiah, U. / Mishra, Suman / Hari Krishna, B. et al. | Springer Verlag | 2024


    Road Traffic Condition Monitoring using Deep Learning

    SASI PRIYA, S. / S, RAJARAJESHWARI. / K, SOWMIYA. et al. | IEEE | 2020


    DEEP LEARNING BASED ROAD CONDITION MONITORING SYSTEM

    A H SRINIVASA / SIDDAIAH SIDDARAJU / BASAVARAJU T G et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    ROAD TRAFFIC CONDITION MONITORING SYSTEM

    YOKOTA KENSUKE | Europäisches Patentamt | 2023

    Freier Zugriff

    Deep Learning-based Road Condition Monitoring System and Method

    Europäisches Patentamt | 2023

    Freier Zugriff