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
+ x6 C& ]; ?% Gaccurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
, P6 \. ?6 g7 B, ]5 troad, whether it’s traffific signals, accidents, rallies, even repairing
" Q9 M9 ~' ~5 J- Yof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
( w3 E$ t* w: M& l3 emore daily life situations which can affect traffific then, a driver
+ p4 Y! J! [2 y- L6 ^or rider can make an informed decision. Also, it helps in the
future of autonomous vehicles. In the current decades, traffific data
* C+ Y8 K- h6 u# p& k1 U* L8 X' g& Hhave been generating exponentially, and we have moved towards
/ e" ~) b8 H! v3 }the big data concepts for transportation. Available prediction
3 @+ }3 b6 b; u0 r/ p6 q; Mmethods for traffific flflow use some traffific prediction models and
4 V$ O5 ]0 D/ l: r3 y/ qare still unsatisfactory to handle real-world applications. This fact
4 E9 x. I$ g1 e1 W& |& Y. E  Tinspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
4 [0 Q4 z5 L* A- n9 o; w7 L* Q4 Dflflow accurately because the data available for the transportation
, n4 J) H& m! f" o* r' ysystem is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
1 [) S- C* S2 rto analyse the big-data for the transportation system with
9 ^  a1 a' U, q, @2 J: ?much-reduced complexity. Also, Image Processing algorithms are
3 }( n2 Z7 J# E1 kinvolved in traffific sign recognition, which eventually helps for the
" l3 J9 W1 g, T. z: T  Gright training of autonomous vehicles.


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