Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

# G) b: q8 ?" ~4 |5 r" m+ V
- B5 P# x, I7 K
This paper aims to develop a tool for predicting
% q. S+ D0 C" v4 Faccurate 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
8 D3 p) c9 a/ Q# k5 ~) pof roads that can cause a jam. If we have prior information
7 z# H: R1 ?8 V0 o1 ywhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
! P1 b  X) i! e; j0 Bor rider can make an informed decision. Also, it helps in the
% W2 s8 K2 W! n# Pfuture of autonomous vehicles. In the current decades, traffific data
4 i: G- a) V0 G" I9 |have been generating exponentially, and we have moved towards
6 }8 v8 k7 X" e$ e4 }the big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
2 E+ P# G- g& K1 M; y  C) r* xare still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
: U9 M1 d/ g( p* C, Y  I6 Z1 rthe traffific data and models.It is cumbersome to forecast the traffific
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
to analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
" l( p* ^4 u; G4 |" Sinvolved in traffific sign recognition, which eventually helps for the
# C  H  [* c* b5 Wright training of autonomous vehicles.
4 B* r& i" P' {, \" k

7 G: E  t; F, \+ v4 ^$ B* J

* ^5 I. G+ y6 F# U8 F. e
6 O0 w) i/ s: Y) Y) m. q