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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This paper aims to develop a tool for predicting
  L- t- ^9 r: G  e6 q' _+ aaccurate 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
9 @# o3 X8 ]' Bof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
* b8 ^& ~3 x8 L. Zmore daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
/ T8 Q4 \" ^) M8 H% k/ f5 lfuture of autonomous vehicles. In the current decades, traffific data
* v9 D" \7 r  m9 v+ p* o; ]! k. khave been generating exponentially, and we have moved towards
9 F7 {/ [& m$ Athe big data concepts for transportation. Available prediction
9 Y2 r4 B5 a' x& J# Mmethods 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
" ~+ ]' R# R+ m5 {- f8 D/ Q- fthe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
' ^& S1 H# K1 w: l) p3 |* x$ O5 psystem 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
) u( H8 N# \2 Minvolved in traffific sign recognition, which eventually helps for the
. a* X0 x# F3 V7 A" S6 Yright training of autonomous vehicles.
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