In today’s era, it has become easier to collect traffic data using GPS enabled devices. In cities, a good proportion of people use Map Services like Google Maps in their daily commute. Google’s Geocoding and Direction APIs can easily track users’ geolocation and the route they are going to take in their commute. The scope of this work is to develop and create a mobile application which aids in the collection of GPS data from the users and use it to allocate fair green light timings, schedule proper lane departures and suggest lanes accordingly to the users approaching an intersection. Assuming that the drivers will follow the suggestion of the mobile application, this will reduce congestion and delays on roads with high traffic flow by prioritizing and assigning them the maximum green light time. The lanes with little traffic are assigned less green light time while the ones with no traffic are skipped for the next cycle. In order to validate our work, we used AnyLogic [9] to simulate different traffic scenarios. We found that our method allocates fair green light timings and lane scheduling, thus significantly reducing the traffic congestion on the roads with higher traffic flow. The main motivation behind the work is to develop a system which is cheap to implement and can be easily scaled without requiring expensive equipment and minimizing unnecessary congestion on the roads.


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    Titel :

    GPS Based Adaptive Traffic Light Timings and Lane Scheduling


    Beteiligte:
    Sharma, Krishna Kant (Autor:in) / S, Indu (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    1033933 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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