Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

( h  }9 p% j# ^3 c: c% T' V

System using Machine Learning

6 w/ ~* N/ P! |1 x6 m
5 z7 `% E  S* S) w

, k0 i& Z8 C3 W: f0 V" [4 E$ {
7 h+ G  ^2 I, H4 a
. i1 @  e+ h; M4 Z0 m( T# zThis 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
. P4 C1 G, R* X" P6 p; Proad, whether it’s traffific signals, accidents, rallies, even repairing
of roads that can cause a jam. If we have prior information
  M+ p* j, v9 _7 Twhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
future 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
methods for traffific flflow use some traffific prediction models and
1 t) w3 R! o) z6 L7 R5 I6 Fare 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$ \% y& C, j" P- _- Vflflow 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
% B! E  l0 s1 X3 ^0 gto analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
. T; S& b% I2 D: `% L  Jinvolved in traffific sign recognition, which eventually helps for the
1 Y, n( Q$ x9 _3 Zright training of autonomous vehicles.


- {+ m$ ~( h/ s- p