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
5 l' H& u4 r8 `accurate and timely traffific flflow Information. Traffific Environment
  p6 f2 E- n) R/ oinvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
& t' H- o' E! {0 i. W) n& l6 Yof 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
- x- S5 z9 D3 Xor rider can make an informed decision. Also, it helps in the
. x4 q. f* ~4 U) [4 \. I* A+ dfuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
$ W8 M, e* j/ h7 }& H# pmethods for traffific flflow use some traffific prediction models and
- i' K$ I9 o' w# H8 D5 i+ dare still unsatisfactory to handle real-world applications. This fact
0 {/ S& }( G- zinspired us to work on the traffific flflow forecast problem build on
( M1 v, k6 O7 c! Y" ythe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
8 Y" S; \: c/ G# ?& `system is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
5 i5 ~) ?  X* m: L2 M3 Gto analyse the big-data for the transportation system with
7 M/ u( \4 P9 Y$ e7 d* _much-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
: x9 R$ h/ U* X; Z$ Oright training of autonomous vehicles.
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