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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& H8 v' w; {) N) ]& {& OThis paper aims to develop a tool for predicting
accurate and timely traffific flflow Information. Traffific Environment
( I% I7 `# o7 a9 \9 J2 u9 rinvolves everything that can affect the traffific flflowing on the
' b- D0 v3 e* A; T  droad, whether it’s traffific signals, accidents, rallies, even repairing
of roads that can cause a jam. If we have prior information
, H# B( u' F$ N7 |which is very near approximate about all the above and many
more daily life situations which can affect traffific then, a driver
: X, C. i* M! J2 Yor rider can make an informed decision. Also, it helps in the
$ ~$ U  [; L1 c4 [; @7 Yfuture of autonomous vehicles. In the current decades, traffific data
2 \  c6 {6 n: u, j! Phave 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
& P) o9 p! F. W2 X, D$ w* I: @7 ~are still unsatisfactory to handle real-world applications. This fact
$ b8 a7 w& l/ @3 Cinspired 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
system is insanely huge. In this work, we planned to use machine
/ o% C, z1 I0 d2 ]( W2 mlearning, genetic, soft computing, and deep learning algorithms
; o; ?, S, L2 o0 u4 O3 oto analyse the big-data for the transportation system with
# N/ N6 K* q5 [' t% hmuch-reduced complexity. Also, Image Processing algorithms are
involved in traffific sign recognition, which eventually helps for the
7 Y0 `$ S  L" M/ K- [, E# \% Qright training of autonomous vehicles.

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