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
involves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
& W, ^6 m0 z3 `of roads that can cause a jam. If we have prior information
which 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
future of autonomous vehicles. In the current decades, traffific data
6 E) \6 t4 c0 S2 D3 K  R/ l, x) Ahave been generating exponentially, and we have moved towards
  h& ?4 J% U/ v" mthe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
. G* \. d0 T* v( [7 ?inspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
' W3 }5 f. L: Y; e8 t( Fflflow accurately because the data available for the transportation
, V+ o0 e1 n4 @2 s# q% i- asystem 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
7 [9 H; I% G& H4 r: Zmuch-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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