  Traffic data collection has been an overwhelming task for researchers as well
as authorities over the years. With the advancement in technology and
introduction of various tools for processing and extracting traffic data the
task has been made significantly convenient. Data from Sky (DFS) is one such
tool, based on image processing and artificial intelligence (AI), that provides
output for macroscopic as well as microscopic variables of the traffic streams.
The company claims to provide 98 to 100 percent accuracy on the data exported
using DFS tool. The tool is widely used in developed countries where the
traffic is homogenous and has lane-based movements. In this study, authors have
checked the veracity of DFS tool in heterogenous and area-based traffic
movement that is prevailing in most developing countries. The validation is
done using various methods using Classified Volume Count (CVC), Space Mean
Speeds (SMS) of individual vehicle classes and microscopic trajectory of probe
vehicle to verify DFS claim. The error for CVCs for each vehicle class present
in the traffic stream is estimated. Mean Absolute Percentage Error (MAPE)
values are calculated for average speeds of each vehicle class between manually
and DFS extracted space mean speeds (SMSs), and the microscopic trajectories
are validated using a GPS based tracker put on probe vehicles. The results are
fairly accurate in the case of data taken from a bird eye view with least
errors. The other configurations of data collection have some significant
errors, that are majorly caused by the varied traffic composition, the view of
camera angle, and the direction of traffic.
