The road traffic management system and its inconsistencies are one of issues to be addressed. According to Google surveys and reports every year, people face many difficulties on road. Traffic congestions due to inefficient signal management systems which not only causes time delay and stress but also costs health of traffic police due to inhalation of polluted air. Therefore, inefficient traffic management causes unwanted wait times and congestion which produces a lot of CO2, a huge amount practically. Due to inefficient traffic management system, Emergency vehicles such as Ambulance, Fire Engines, and Rescue vehicles may tend to lose lots of valuable seconds. During absence or error in the system causes the signals to go offline, thus causing chaos and accidents. It is a necessity to calculate the real time road traffic density for improved traffic management due to the ever-increasing population of vehicles every day. One of the most impacting elements is the efficiency of the traffic controller as a result there are lot of opportunities for improvement and its demand increases. In our proposed system, live CCTV footage is given as input and vehicles are detected with the help of artificial intelligence. The input undergoes image processing techniques, and the vehicle density is calculated. The green signal time is also computed from the obtained traffic density to avoid long waiting time at road intersections.


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    Titel :

    Density-Based Traffic Control System Using Artificial Intelligence


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Gupta, Deepak (Herausgeber:in) / Khanna, Ashish (Herausgeber:in) / Hassanien, Aboul Ella (Herausgeber:in) / Anand, Sameer (Herausgeber:in) / Jaiswal, Ajay (Herausgeber:in) / Sabeenian, R. S. (Autor:in) / Ramapriya, R. (Autor:in) / Swetha, S. (Autor:in)


    Erscheinungsdatum :

    08.11.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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