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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This paper aims to develop a tool for predicting
accurate 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
( N% ]1 g7 e3 V0 ~! Eof roads that can cause a jam. If we have prior information
, Z& J0 O7 ]3 }# a4 u! Kwhich 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
1 @) Y: E  r. M* Y% y$ h6 o, dfuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
% @( i! t/ E1 G; W7 o7 Lthe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
0 j( }" F0 P- u. a$ |* e( Dare still unsatisfactory to handle real-world applications. This fact
$ N9 W8 E$ S  N) M' Iinspired us to work on the traffific flflow forecast problem build on
9 `) t- _* e$ ]the traffific data and models.It is cumbersome to forecast the traffific
& C$ s9 I% L- m$ hflflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
5 y* h* P0 U0 i% W( M8 wlearning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with
7 [" `2 t8 x' W1 tmuch-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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