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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( w9 e0 l- y9 Z4 S$ j- A3 a( |2 BThis paper aims to develop a tool for predicting
) Y) [7 _" l4 i2 B6 h1 H- ^accurate and timely traffific flflow Information. Traffific Environment
7 r/ j3 Q% j" O- I. ninvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
7 t# F  p1 U' L/ E7 v6 Oof roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
* s7 ]& M( S  f5 A. Kmore daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
3 D: j1 X- o2 B8 ?- [future of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
5 f7 A8 L0 M1 y+ gthe 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
5 |7 E/ y' E/ f0 _$ {0 u& u% }4 zinspired us to work on the traffific flflow forecast problem build on
& C7 O! f* `1 `/ Vthe 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
9 v7 e- ]4 F, [; E: z7 l9 kmuch-reduced complexity. Also, Image Processing algorithms are
) e7 y$ Q  I0 F8 Tinvolved in traffific sign recognition, which eventually helps for the
5 U  L0 F* O! Z5 t. cright training of autonomous vehicles.
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