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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$ \- b9 z2 A* v6 n0 PThis paper aims to develop a tool for predicting
- g; M$ h! n2 m) i  Taccurate and timely traffific flflow Information. Traffific Environment
) t$ D/ x9 r# U4 d) N1 K# kinvolves everything that can affect the traffific flflowing on the
( ?3 P; ?8 A0 j6 G3 o5 o8 Uroad, whether it’s traffific signals, accidents, rallies, even repairing
5 X  o) M$ e/ Q7 m6 Fof 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
: h* x! G6 e0 H+ g- D8 |1 gor 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
% R3 j9 G$ C# othe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
) d1 \  @# F: R% cinspired us to work on the traffific flflow forecast problem build on
+ U) l) P: d9 _8 {  u. ^" ethe traffific data and models.It is cumbersome to forecast the traffific
* A7 @8 \: K5 Y4 U& M6 U; Pflflow accurately because the data available for the transportation
% R' a" l9 h/ Y' B, fsystem 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
! a6 P" m3 t6 K' C0 E' C( _involved in traffific sign recognition, which eventually helps for the
1 H. a6 V, @4 q+ E6 Kright training of autonomous vehicles.
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