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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3 ~5 {8 W) L' ~This paper aims to develop a tool for predicting
) P( g, S: [) u" d2 m, p* E/ Q' g1 L; paccurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
6 E4 ]; Q  y2 H9 Xof roads that can cause a jam. If we have prior information
; D9 K; c: s$ V. Y9 c/ pwhich is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
  |' n1 b& t! a( l: q' [0 ^5 Yor rider can make an informed decision. Also, it helps in the
$ X1 T* _: h; N7 Z$ nfuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
  M' H) _7 _3 m" V& P& G! Tthe big data concepts for transportation. Available prediction
2 M! f2 F# O& A9 n* s% Smethods 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
the traffific data and models.It is cumbersome to forecast the traffific
: L9 ?4 V" d9 N, p* Wflflow accurately because the data available for the transportation
7 H) I) ]" H- [) f7 h9 Qsystem is insanely huge. In this work, we planned to use machine
learning, genetic, soft computing, and deep learning algorithms
: e9 k, i& Q7 k) Uto analyse the big-data for the transportation system with
+ j& O# p3 p. `. L! Smuch-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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