In the face of mounting challenges related to traffic congestion and road safety in modern urban areas, this paper introduces an innovative real-time traffic control system that leverages the capabilities of computer vision and embedded computing technologies. To achieve this, the system harnesses the power of OpenCV, a robust computer vision library, for the purpose of live vehicle detection using pre-trained cascade classifiers. By integrating cameras with Raspberry Pi devices, the system can capture real-time traffic footage, enabling the immediate analysis of traffic density and flow in multiple directions within the urban landscape. The core functionality of the system lies in its ability to process the captured video frames in real-time. By doing so, it can accurately detect vehicles, evaluate traffic flow conditions, and identify congested routes. This critical information is then employed to dynamically adjust traffic signals in response to the detected traffic conditions. These adjustments optimize traffic control measures, leading to the alleviation of congestion, and ultimately contributing to an enhancement in road safety. This paper provides a comprehensive exploration of the system architecture, the underlying algorithms, and integration details. Through this detailed discussion, the paper demonstrates the remarkable effectiveness of this approach in the realm of urban traffic management. By seamlessly combining computer vision and embedded computing technologies, this innovative traffic control system not only provides real-time insights into traffic conditions but also actively responds to these conditions to ensure a smoother and safer flow of vehicles through urban streets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Density Based Traffic Management System


    Beteiligte:
    S, Amaranatha Sasthry (Autor:in) / Sundar R, Shyam (Autor:in) / M, Sri Ganesh (Autor:in) / Kumar R, Anand (Autor:in) / M, Jayalakshmi (Autor:in)


    Erscheinungsdatum :

    2024-02-24


    Format / Umfang :

    1128970 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Management System Based on Density Prediction Using Maching Learning

    Sankaranarayanan, Suresh / Omalur, Sumeet / Gupta, Sarthak et al. | Springer Verlag | 2021


    TRAFFIC DENSITY OPTIMIZATION SYSTEM AND TRAFFIC DENSITY OPTIMIZATION METHOD

    TSUKAHARA YUI | Europäisches Patentamt | 2015

    Freier Zugriff

    Density based Management Concept for Urban Air Traffic

    Geister, Dagi / Korn, Bernd | IEEE | 2018


    Vehicle Density Based Traffic Control System

    Anand Gaikwad / Shreya / Shivani Patil | BASE | 2018

    Freier Zugriff

    Density-Based Remote Override Traffic Control System

    Varshney, Gunjan / Jaiswal, Anshika / Mittal, Udit et al. | Springer Verlag | 2021