Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

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System using Machine Learning

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( _& A5 w+ @% YThis paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
. o. K$ o# Z* hof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
( m1 D0 b0 J# y" ^more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
. G" c+ w/ w- j8 q* S  ?. Lfuture of autonomous vehicles. In the current decades, traffific data
2 K! Y* N& F, N8 bhave been generating exponentially, and we have moved towards
$ x4 M4 _" q" C8 t) ^* nthe big data concepts for transportation. Available prediction
, a9 @1 K  s$ Y1 c% {methods for traffific flflow use some traffific prediction models and
. D' r9 J3 k8 M$ Fare still unsatisfactory to handle real-world applications. This fact
# a' z; }, v% k9 s' I6 y9 ?' ~inspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
6 d9 o& B( a0 `' M6 y9 Xflflow accurately because the data available for the transportation
1 c- _, n0 o: D$ r; N8 ^- X- z$ Jsystem 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
much-reduced complexity. Also, Image Processing algorithms are
: |( m8 v; q9 Linvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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