Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

: L8 |) B9 A2 R



This paper aims to develop a tool for predicting
, o2 z5 W( X( O2 N8 t! ^$ A% Baccurate and timely traffific flflow Information. Traffific Environment
% C  l4 e$ M: s' C( t, Binvolves 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
& B8 d' F, V& r& G! f( _; V. emore 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
% p' y/ u! e/ d: Xhave been generating exponentially, and we have moved towards
' B9 f8 G# h/ i; {( g" [) Athe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
) V4 T0 t5 ~+ ]0 F' finspired us to work on the traffific flflow forecast problem build on
0 s( P" y* Y& }' c: U. D5 |the traffific data and models.It is cumbersome to forecast the traffific
& H- @( D4 @# H& P6 {6 ~flflow 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
5 D- ~6 F1 \4 J9 ~) G: dto analyse the big-data for the transportation system with
8 U: _. X- U5 @- m3 Z( ?3 qmuch-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
$ k- O5 W; m) u0 {