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
2 e- o0 @+ F2 p5 u$ r% Qaccurate 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 ?) f0 ]/ K& ^: x. S% Zof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
2 f/ e3 {1 @+ f' T! `4 g2 Jmore daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
- D; W# X3 _# T5 n4 d5 C$ F; f& sfuture of autonomous vehicles. In the current decades, traffific data
9 q% l1 @( b3 Z. chave been generating exponentially, and we have moved towards
) ?- c- Y- J& v6 i& {: Pthe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
' |( r: \  S- H+ f5 D$ O* rare still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
, \& Y# w6 X0 tthe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
system 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
, V( O6 c/ P- Q3 K; D1 [5 M# p0 linvolved in traffific sign recognition, which eventually helps for the
+ L2 i5 C9 {7 U) K& S2 ~5 U; oright training of autonomous vehicles.
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