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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/ W9 t3 q1 e8 z2 M% q0 T7 }7 Q) DThis 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
. S3 \+ }3 J5 Y6 uroad, 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
, m" p7 g# V- ?* f0 z7 t  v# b* bmore daily life situations which can affect traffific then, a driver
or 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
% u  Q8 |* |* u7 Vthe big data concepts for transportation. Available prediction
methods for traffific flflow use some traffific prediction models and
! }$ X% u/ n/ O+ T! b1 `are still unsatisfactory to handle real-world applications. This fact
% ~, Z; u* V$ M4 @7 d. xinspired us to work on the traffific flflow forecast problem build on
the traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
& C4 n( g. ?$ h3 U7 Lsystem is insanely huge. In this work, we planned to use machine
/ s: b, S* i. x( ]/ @1 N1 Zlearning, genetic, soft computing, and deep learning algorithms
to analyse the big-data for the transportation system with
" N; @& R, p5 Z  i; Z0 W- B- umuch-reduced complexity. Also, Image Processing algorithms are
' r! K4 o0 }7 x/ d2 B; @, \involved in traffific sign recognition, which eventually helps for the
, {$ N3 d: F! g$ v5 Zright training of autonomous vehicles.