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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8 t" Z& D2 O1 a" D. h( `/ fThis 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
5 F8 l6 k* V- j3 v4 V2 \2 Oroad, whether it’s traffific signals, accidents, rallies, even repairing
of roads that can cause a jam. If we have prior information
! C5 Q' R% }2 pwhich 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 q. [  d. [8 k% R5 _4 jfuture of autonomous vehicles. In the current decades, traffific data
& e% t& `5 D9 ^/ i$ [5 O  i7 U5 ahave been generating exponentially, and we have moved towards
3 |, B7 y$ U# v& B& dthe big data concepts for transportation. Available prediction
9 V* f( c) B# `methods for traffific flflow use some traffific prediction models and
are still unsatisfactory to handle real-world applications. This fact
  ]/ W' r: I: {: {$ U2 pinspired us to work on the traffific flflow forecast problem build on
' [8 l* k6 Z: lthe traffific data and models.It is cumbersome to forecast the traffific
flflow accurately because the data available for the transportation
# O6 l7 G/ y, O0 csystem is insanely huge. In this work, we planned to use machine
; _) F1 Y) l, j- v& N$ E8 t7 Wlearning, genetic, soft computing, and deep learning algorithms
, [/ ^8 h; l6 ~3 h9 J$ vto analyse the big-data for the transportation system with
much-reduced complexity. Also, Image Processing algorithms are
7 V4 `7 W+ l% j0 I: uinvolved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.

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