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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* O8 X/ ?4 x: K2 F! J' Z; a* nThis paper aims to develop a tool for predicting
4 L. f1 @6 v% F. w) E) i2 i1 Waccurate and timely traffific flflow Information. Traffific Environment
# y3 k. m$ ?- [" K2 V* @5 Z, minvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
0 V* P, }7 W; S, a: G% x1 Vof roads that can cause a jam. If we have prior information
2 D: u5 Z+ i# bwhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
6 c6 B- j+ \6 Afuture of autonomous vehicles. In the current decades, traffific data
/ ]8 C; e7 U/ i1 uhave been generating exponentially, and we have moved towards
3 r3 n% e8 _* u9 N% S( Xthe 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
inspired us to work on the traffific flflow forecast problem build on
2 A! ]6 s8 P* C! x4 f* othe 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
/ k- z# S( N, k, j# e+ Pmuch-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.
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