Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

# x8 b% m" q: o3 ]. ]' e( X

1 A  w6 l/ R' f
- A- a6 U. e! K" I, u+ _
This paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
9 p5 @( r! c% f/ I$ Finvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
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
5 ~5 r' W% m, b! |8 j8 t. Sor rider can make an informed decision. Also, it helps in the
- ^/ U8 f3 N& yfuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
2 I2 I4 h/ O# q' d2 @the big data concepts for transportation. Available prediction
4 }  A9 @. }8 c- i# mmethods for traffific flflow use some traffific prediction models and
! {( X0 a3 c2 Iare still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
/ D' C- q- Y+ l/ h" v5 `# Tthe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
' |. o8 W$ z9 R# @3 O0 bsystem is insanely huge. In this work, we planned to use machine
3 F3 |' z4 y7 E$ Ilearning, genetic, soft computing, and deep learning algorithms
0 b, ?0 P0 F+ o; l& H% ~* e1 E0 \to analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
/ s, s* I% y* A/ Z. M" x: M# Dinvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
4 b  m* z7 S* {  p$ c. x/ ]
% g  d$ h3 K, ]1 B; q( V4 s. Z