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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6 ]/ g& [* D- m3 f0 t! RThis paper aims to develop a tool for predicting
+ M$ {3 {6 g! s0 O# ~8 ]1 oaccurate 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
8 G& R. e# ]+ h6 @2 cof 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
5 [* [" j0 M* x" {4 A1 Wor rider can make an informed decision. Also, it helps in the
/ ?3 b. ]( i, a% @future of autonomous vehicles. In the current decades, traffific data
6 |4 ^: \' l; {+ V& T: Y" {have been generating exponentially, and we have moved towards
$ E; D% [) i4 g! x8 Ethe 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
inspired us to work on the traffific flflow forecast problem build on
* G/ J% G  E; X: G" f% pthe traffific data and models.It is cumbersome to forecast the traffific
# g7 U: M0 l9 Vflflow accurately because the data available for the transportation
; U7 k' R8 N1 H# Psystem is insanely huge. In this work, we planned to use machine
2 r  `% x+ k8 p) flearning, 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
involved in traffific sign recognition, which eventually helps for the
right training of autonomous vehicles.

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