Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

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System using Machine Learning

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This paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
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
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
future of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
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 fact
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
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
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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