Traffic Prediction for Intelligent Transportation System using Machine Learning

杨利霞

Traffific Prediction for Intelligent Transportation

System using Machine Learning

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This paper aims to develop a tool for predicting
* T' E; Y, y2 ~# Daccurate and timely traffific flflow Information. Traffific Environment
% C3 Z7 x# W& x' x9 Y& linvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
6 q- d( a3 H1 u* E* V8 ^1 yof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
+ A: a' t5 U- E5 W/ v; i  f) M) Wmore daily life situations which can affect traffific then, a driver
) _$ ?$ y$ u$ }. Cor rider can make an informed decision. Also, it helps in the
% h! p) Y; u9 l' `, j) b6 {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
methods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
2 C! X( l$ q- @, C8 iinspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
+ `# j* |5 b+ f' [* w! b/ ]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
, c! g/ P1 k/ a% oto analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
8 a3 A( m- S, n6 W( ~0 ~; `3 Z2 Xright training of autonomous vehicles.
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