Traffific Prediction for Intelligent Transportation 0 d+ D9 n% \& R/ g) W& U
System using Machine Learning K0 K* W) k; [3 n1 v3 P' L
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This paper aims to develop a tool for predicting) U# n0 j& @! C! I# k' h2 V; S8 P2 r
accurate and timely traffific flflow Information. Traffific Environment e2 I" l& y+ W/ S Y
involves everything that can affect the traffific flflowing on the$ G4 [& Z4 c1 y1 _5 H
road, whether it’s traffific signals, accidents, rallies, even repairing
( N% ]1 g7 e3 V0 ~! Eof roads that can cause a jam. If we have prior information
, Z& J0 O7 ]3 }# a4 u! Kwhich is very near approximate about all the above and many( F% x+ q# R; {( U
more daily life situations which can affect traffific then, a driver, N# M0 @- r& e
or rider can make an informed decision. Also, it helps in the
1 @) Y: E r. M* Y% y$ h6 o, dfuture of autonomous vehicles. In the current decades, traffific data, G4 ]; J4 l* G& X& t7 T1 l
have been generating exponentially, and we have moved towards
% @( i! t/ E1 G; W7 o7 Lthe big data concepts for transportation. Available prediction* g& i" G2 g) s" x- b# N. @; S
methods for traffific flflow use some traffific prediction models and
0 j( }" F0 P- u. a$ |* e( Dare still unsatisfactory to handle real-world applications. This fact
$ N9 W8 E$ S N) M' Iinspired us to work on the traffific flflow forecast problem build on
9 `) t- _* e$ ]the traffific data and models.It is cumbersome to forecast the traffific
& C$ s9 I% L- m$ hflflow accurately because the data available for the transportation! F s- L6 ?% x
system is insanely huge. In this work, we planned to use machine
5 y* h* P0 U0 i% W( M8 wlearning, genetic, soft computing, and deep learning algorithms, P( G( o4 ]+ c" v
to analyse the big-data for the transportation system with
7 [" `2 t8 x' W1 tmuch-reduced complexity. Also, Image Processing algorithms are \! ?+ K1 l0 m
involved in traffific sign recognition, which eventually helps for the& t9 j& V' o; g" k( ~/ y3 B! C
right training of autonomous vehicles.
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