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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This paper aims to develop a tool for predicting
: H! o# t  q) v9 r, maccurate and timely traffific flflow Information. Traffific Environment
) w" k& Y( l! b0 b3 G% _3 }+ \8 Vinvolves everything that can affect the traffific flflowing on the
5 k; o: _5 Z: L/ d  ~! I1 j! wroad, whether it’s traffific signals, accidents, rallies, even repairing
6 g- a9 H; O9 C9 L. R4 lof 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
or rider can make an informed decision. Also, it helps in the
$ K% ~4 R- N" B- z1 Ufuture of autonomous vehicles. In the current decades, traffific data
have been generating exponentially, and we have moved towards
! H* G1 v& g& ?$ p9 R+ ^the big data concepts for transportation. Available prediction
% \! n' @* F1 V: \" |methods for traffific flflow use some traffific prediction models and
) t9 o$ Y5 n; ?1 W9 ]9 |are still unsatisfactory to handle real-world applications. This fact
& p* S0 K# E( {$ O- W6 R; \# [inspired us to work on the traffific flflow forecast problem build on
$ N0 |( s& q# Z( P! cthe traffific data and models.It is cumbersome to forecast the traffific
8 L4 s. a0 U4 u" g0 r0 h' Tflflow accurately because the data available for the transportation
system is insanely huge. In this work, we planned to use machine
$ F- U- w8 i, C3 c% d; jlearning, genetic, soft computing, and deep learning algorithms
2 E+ W+ C" g7 V6 l7 oto analyse the big-data for the transportation system with
! V2 n" x5 n* ?. A( L; [7 fmuch-reduced complexity. Also, Image Processing algorithms are
1 n; `' U9 O; c! L/ V- e" G4 Qinvolved in traffific sign recognition, which eventually helps for the
, l/ t/ X* ?0 }# Kright training of autonomous vehicles.
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