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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) w: B8 d9 F  B* ^' e" ?- rThis 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
of roads that can cause a jam. If we have prior information
( G8 k# T. Y5 B9 L- u! lwhich 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
4 {" o* s8 A2 }% \future of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
! t" F+ g# g" sthe big data concepts for transportation. Available prediction
7 {" |8 A' H7 pmethods for traffific flflow use some traffific prediction models and
2 y+ E) }4 G9 r: w6 F9 f  s6 aare still unsatisfactory to handle real-world applications. This fact
4 g/ E& j& z6 S# k, J4 Q9 z) linspired us to work on the traffific flflow forecast problem build on
1 x  _$ d0 ?$ dthe traffific data and models.It is cumbersome to forecast the traffific
7 e! n. d$ a( \- {8 D& d& {flflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
8 H4 A0 o" x3 P  Z+ r* ulearning, genetic, soft computing, and deep learning algorithms
7 G. @3 v( V' O$ ]! gto 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
  W3 I1 l# G* _6 K! l. qright training of autonomous vehicles.
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