Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

0 D! ^0 b5 U, [  ]
1 }; E/ T$ g& s4 e6 W4 y. y& Q( ?. R0 E


4 r( M" k+ Y/ B( s' eThis paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
& M7 F9 h6 S* z& y, l' iinvolves everything that can affect the traffific flflowing on the
! G3 q- D# d' p( ?. Mroad, 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
4 C7 Q  C; ?% m2 S/ Cmore daily life situations which can affect traffific then, a driver
% n3 Z9 D5 I* E3 nor rider can make an informed decision. Also, it helps in the
future of autonomous vehicles. In the current decades, traffific data
& y) b; @/ J" b) o! Z, mhave been generating exponentially, and we have moved towards
3 H% G. |7 k4 Y1 Z0 a+ qthe big data concepts for transportation. Available prediction
" Z# _" Y+ x4 S$ O# g/ E1 m) Fmethods for traffific flflow use some traffific prediction models and
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
flflow accurately because the data available for the transportation
( o. O* P4 l- r8 ^/ v! T& Isystem is insanely huge. In this work, we planned to use machine
3 \1 {" |1 Q. o; k0 z/ Rlearning, genetic, soft computing, and deep learning algorithms
% j0 F5 _7 O% u: U" a3 M# Eto analyse the big-data for the transportation system with
, d8 p( u. V3 ?- imuch-reduced complexity. Also, Image Processing algorithms are
4 @, j0 z. W4 w9 kinvolved in traffific sign recognition, which eventually helps for the
  x% u) x6 n7 X6 I2 g: m; Yright training of autonomous vehicles.
2 t' E* n/ `" V, F1 p/ @
) W( F6 z! f/ ^* X) i9 R