Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

- A9 H' {7 J% Q$ l% h



9 `0 I" ^: I/ Q; b, ^/ B7 ]# a
$ @' B% E7 y  w' iThis paper aims to develop a tool for predicting
accurate 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 }& J( h0 [% xof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
  u( r" @3 t1 ymore daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
& I5 S( m) z/ ?0 jfuture of autonomous vehicles. In the current decades, traffific data
7 L$ w3 o6 i+ @" K; qhave been generating exponentially, and we have moved towards
the big data concepts for transportation. Available prediction
: f; q3 `. c$ H8 Y5 n  Emethods for traffific flflow use some traffific prediction models and
, |, {0 o2 J" N5 H9 P* uare still unsatisfactory to handle real-world applications. This fact
- M/ m$ e+ b# a' {: ~1 \. `inspired us to work on the traffific flflow forecast problem build on
7 r3 B4 m; Z9 ]* h" athe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
% q: m; l6 O+ }/ I" z6 I( r8 psystem is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
: e5 }$ A6 D* _/ j- K( zto analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
3 b" t" [7 L9 Linvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.