Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

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This paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
. X8 c  U% r7 l0 r! h, ~involves everything that can affect the traffific flflowing on the
% s2 ?! x6 D6 `& H+ hroad, whether it’s traffific signals, accidents, rallies, even repairing
- l5 N! ~& Z) Sof roads that can cause a jam. If we have prior information
1 v! ?( ?$ }* f3 u& y' |8 m9 V3 owhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
8 g( @. h5 }& b* E; ofuture of autonomous vehicles. In the current decades, traffific data
+ H$ y# I, }- W6 l; i5 M4 C6 V5 shave been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
# o3 [% n/ U: z3 d: h- I  rmethods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
! Z4 _" u' h/ f# N* xflflow accurately because the data available for the transportation
, Q! X. z" t7 s6 e! W5 O" O5 g% ~system is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with
' C! e+ Q5 W' l! ]9 jmuch-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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