The vehicle population in the world is growing rapidly. Therefore the urban traffic is increasing in proportion. Also, it increases associated problems such as accidents, stress, and pollution. According to the statistics in the Sri Lankan city of Colombo, the average speed of a vehicle would be around 10 km per hour. However to reduce this traffic most cities used traffic light systems. A traffic light system controls the flow of automobiles in a junction, pedestrian crossing, or other special locations. The city traffic is changing within the day. In most cities, traffic increases in the morning and evening. Due to this timely variation of traffic flow, most traffic lights are not able to control or minimize this traffic by changing to a pre-defined logic or a timer. In most of the cases, police officers manually adjust these traffic lights. This is inappropriate and day by day the city traffic will increase. The main research objective is to make the traffic light systems more efficient by monitoring and dynamically changing the waiting time of the traffic lights in real-time. In most of the cases, the traffic lights time is adjusted manually according to the traffic size, or sometimes those timers cannot adjust. So this is the main problem identified by the authors. Therefore, in this research, it is proposed to implement a dynamic traffic light controlling system by using Google Maps and IoT. This proposed solution is expected to avoid or minimize these unnecessary traffic jams by identifying traffic density and dynamically change the waiting time of the traffic lights. Moreover, emphasis was placed on the easie of applicability of this system directly to the present traffic light systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Dynamic Traffic Light Controlling System Using Google Maps and IoT


    Beteiligte:


    Erscheinungsdatum :

    2020-12-15


    Format / Umfang :

    2239642 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Empirical MFDs using Google Traffic Data

    Knoop, Victor L. / van Erp, Paul B.C. / Leclercq, Ludovic et al. | IEEE | 2018


    A Dynamic Heads-Up Air Traffic Locator & Collision Advisory Display Using Google Glass

    Rafi, Melvin / Chandrasekaran, Balajikartikeyan / Kusmez, Mehmet et al. | AIAA | 2016


    A Google-Map-Based Arterial Traffic Information System

    Wu, Yao-Jan / Wang, Yinhai / Qian, Dalin | IEEE | 2007



    A web spatial decision support system for vehicle routing using Google Maps

    Santos, Luís / Coutinho-Rodrigues, João / Antunes, Carlos Henggeler | BASE | 2011

    Freier Zugriff