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
+ O+ d" Z$ W0 i  Naccurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
0 F+ ]9 @" {: k1 oroad, whether it’s traffific signals, accidents, rallies, even repairing
# J4 e; C; G+ @* `3 @+ Wof roads that can cause a jam. If we have prior information
; O, c8 D, {0 r; g- ~which is very near approximate about all the above and many
4 }* q, P# F3 Q2 M0 |+ @0 Qmore daily life situations which can affect traffific then, a driver
) _1 d& W1 s5 y) I$ ^1 ]or rider can make an informed decision. Also, it helps in the
1 F- F+ {2 k3 }( u, Qfuture of autonomous vehicles. In the current decades, traffific data
* n" C8 C1 |, j4 Xhave been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
  P+ a* G" V9 n4 f9 A0 r! j% Rmethods for traffific flflow use some traffific prediction models and
: ^: |+ n6 {1 M; q8 e. @9 {are still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
" s" O! `) Z1 e! Z( ~  s% dflflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
6 H# w6 X! _, U# `$ Wlearning, genetic, soft computing, and deep learning algorithms
' S$ M7 B/ m2 I8 s. `' `# cto analyse the big-data for the transportation system with
' n8 N: {% L) i# emuch-reduced complexity. Also, Image Processing algorithms are
7 J2 \! R' c* x- tinvolved in traffific sign recognition, which eventually helps for the
: G% _# v/ p. }/ w7 [4 yright training of autonomous vehicles.

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