In recent years, traffic has emerged as a
ubiquitous problem faced by thousands of commuters on
daily basis. With the, ever-increasing number of vehicles
emerging on road, the problem does not seem to fade away. It
poses a strikingly major conundrum to a large number of
people. Using the analysis we use Twitter as our Database
and aim to find the traffic-prone areas (e.g. In Delhi) so that
people are familiar with the areas, which are highly prone to
Traffic. In this paper, we demonstrate how social media
content can be used to predict real-world outcomes. In
particular, we use tweets made from twitter handles to
forecast traffic prone areas in a specified region. We further
analyze those numbers using linear regression to find which
area is more prone to traffic.
Aastha Khanna : Department Of Information Technology, I.G.D.T.U.W,
Kashmere Gate, New Delhi 110006, India
Tanvi Malhotra : Department Of Information Technology, I.G.D.T.U.W,
Kashmere Gate, New Delhi 110006, India
Sonal Meena : Department Of Information Technology, I.G.D.T.U.W,
Kashmere Gate, New Delhi 110006, India
Dr. Kalpana Yadav : Department Of Information Technology, I.G.D.T.U.W,
Kashmere Gate, New Delhi 110006, India
Twitter
Data Mining
Traffic Police
Linear
Regression
After applying the regression models and analyzing the data
in detail, we realized that traffic prone is an essential
problem faced by 1.8 crores commuters in Delhi on daily
basis.
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