Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

! w7 C( \; k' y3 v% s

System using Machine Learning

7 J" c; K  k9 P5 H5 E" \7 S
" ?- R) Z1 I) T) ^& L$ @! o- c
% m" {0 f/ A. e3 {8 W

This 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
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
or rider can make an informed decision. Also, it helps in the
" ^) h" f3 g& m" xfuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
  F: `* h. X9 s: _  Ethe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
; j) `- o1 e0 nare 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
8 V) U8 p1 ?9 B* i) S( aflflow 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
% E$ t$ n6 b% {0 a. zto analyse the big-data for the transportation system with
% R, K" P. F) @much-reduced complexity. Also, Image Processing algorithms are
7 y& E% D% ?3 o( Rinvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.


2 k* i5 C; B  A
+ M9 w6 N9 Z- W# N: X: F
! J4 s1 O7 c/ l9 h- G