Traffific Prediction for Intelligent Transportation
; r! H1 q8 N; Q, @9 g$ RSystem using Machine Learning
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This paper aims to develop a tool for predicting3 Y& N: k) O0 _' l/ }2 i1 N
accurate and timely traffific flflow Information. Traffific Environment5 [5 s& e, y% e9 W4 w! F
involves everything that can affect the traffific flflowing on the
" I3 z0 e# Q3 j* L- J" S3 yroad, whether it’s traffific signals, accidents, rallies, even repairing/ [+ [3 u" [! C( A% B
of roads that can cause a jam. If we have prior information
8 B6 y* W: o3 l$ `; x0 Xwhich is very near approximate about all the above and many
# o( k( P* |8 |more daily life situations which can affect traffific then, a driver
* |2 ?2 [9 o& Qor rider can make an informed decision. Also, it helps in the+ _: U: b: ^5 b
future of autonomous vehicles. In the current decades, traffific data5 D$ T! a; a4 Y" I$ @4 b" d: m
have been generating exponentially, and we have moved towards1 \' R& N# k9 g1 Q# m
the big data concepts for transportation. Available prediction
) h5 s) t4 d7 Y" Q% Nmethods for traffific flflow use some traffific prediction models and
& e, Z- ?4 N. y; m; u* care still unsatisfactory to handle real-world applications. This fact5 N0 ?& W! `1 A: X2 g) ~, O. b7 d" l
inspired us to work on the traffific flflow forecast problem build on
; W9 p) v9 q X7 n E1 @8 Jthe traffific data and models.It is cumbersome to forecast the traffific
0 h+ K/ R7 ^: ^8 l$ v0 \ Tflflow accurately because the data available for the transportation
: @* h2 T3 z+ b8 A: `; L$ q# M6 qsystem is insanely huge. In this work, we planned to use machine m1 P' d9 S& K) `3 o5 N3 U# ^
learning, genetic, soft computing, and deep learning algorithms
3 g3 t1 J. O" Z9 \5 z1 G8 Eto analyse the big-data for the transportation system with' n# l8 F6 d7 f/ q# c# Q1 e6 v
much-reduced complexity. Also, Image Processing algorithms are
% i' H! q* Z% H; ]# s" }involved in traffific sign recognition, which eventually helps for the
2 Z( i* Y8 Y6 w2 N# |) i% X. `right training of autonomous vehicles.
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