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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% J: c( }8 e2 N' U8 [% W# s9 _3 [, LThis paper aims to develop a tool for predicting
: H; G6 Z( K: a* H- Aaccurate and timely traffific flflow Information. Traffific Environment
involves everything that can affect the traffific flflowing on the
9 e: c" V" p! a4 g. F  E) \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
* E1 `; P" k3 K2 [more daily life situations which can affect traffific then, a driver
or rider can make an informed decision. Also, it helps in the
) e9 S+ _6 @: r$ c& ffuture of autonomous vehicles. In the current decades, traffific data
' N& }1 n6 Q$ q! whave 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
' D7 `- J, I( t% f3 N4 ^are still unsatisfactory to handle real-world applications. This fact
inspired us to work on the traffific flflow forecast problem build on
, _/ u& B7 a5 I2 u/ k; U  Gthe traffific data and models.It is cumbersome to forecast the traffific
4 h8 }5 }, X1 V7 @/ ?3 Rflflow accurately because the data available for the transportation
; t, H2 b' ~) p' Vsystem 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
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.

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