  Qatar expects more than a million visitors during the 2022 World Cup, which
will pose significant challenges. The high number of people will likely cause a
rise in road traffic congestion, vehicle crashes, injuries and deaths. To
tackle this problem, Naturalistic Driver Behavior can be utilised which will
collect and analyze data to estimate the current Qatar traffic system,
including traffic data infrastructure, safety planning, and engineering
practices and standards. In this paper, an IoT based solution to facilitate
such a study in Qatar is proposed. Different data points from a driver are
collected and recorded in an unobtrusive manner, such as trip data, GPS
coordinates, compass heading, minimum, average, and maximum speed and his
driving behavior, including driver's drowsiness level. Analysis of these data
points will help in prediction of crashes and road infrastructure improvements
to reduce such events. It will also be used for drivers risk assessment and to
detect extreme road user behaviors. A framework that will help to visualize and
manage this data is also proposed, along with a Deep Learning-based application
that detects drowsy driving behavior that netted an 82 percent accuracy.
