A comparative study of traffic detection using Google map (GPS) and IoV

Abstract

Everyone knows that time is money and nowadays people spend quite a large amount of time on the roads in their daily life. The reason behind this is nothing but ‘Traffic’. Traffic detection and navigation has gone through many phases over the years. Once we had to depend on traffic prediction and now, we can check real time traffic updates by the means of services like Google Map. Google Map uses GPS to detect traffic. However, the problem with it is, Map uses historical traffic data and the individual GPS device count to detect the congestion of traffic. This may affect the accuracy in terms of vehicles. Depending only on the device count and past records, a proper real time data cannot be generated. The very important aspect of vehicle count, and vehicle size are totally ignored in this approach. Moreover, a real time representation of traffic will also require 24/7 surveillance. Another promising approach to detect traffic is using IoV (Internet of Vehicles) to count the number of connected vehicles rather than counting people in an area. As the world is moving towards IoT, traffic detection can evolve to a great extent with the introduction of IoV as well. In this work, we will be analysing the existing system google uses for traffic detection and compare it with a possible model to be used with the inclusion of IoV. To provide a verdict on which system will be better, we will be using traffic detection reports of google Map as preliminary data and possible outcome of the IoV model analysed.