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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2 Q$ d. L: G- b0 e  N/ B% u$ EThis paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
+ a9 H/ r# b& ^7 K- P9 A4 I- pinvolves everything that can affect the traffific flflowing on the
road, whether it’s traffific signals, accidents, rallies, even repairing
of roads that can cause a jam. If we have prior information
which is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
; @$ I7 v. q6 z- \9 V0 Z6 f5 por 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
1 d: B1 ]( z4 ~; S" s' W& F  }, p; imethods 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
0 V0 ?6 X  t' r: {: Hflflow accurately because the data available for the transportation
2 y! O: p2 z# P" Usystem 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
much-reduced complexity. Also, Image Processing algorithms are
1 B  `" B6 r1 g/ k5 k7 e( R! Tinvolved in traffific sign recognition, which eventually helps for the
4 g) r  z2 w" w/ l$ j; E" q' T& vright training of autonomous vehicles.

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