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标题: Traffic Prediction for Intelligent Transportation System using Machine Learning [打印本页]
作者: 杨利霞 时间: 2020-11-12 16:27
标题: Traffic Prediction for Intelligent Transportation System using Machine Learning
Traffific Prediction for Intelligent Transportation
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System using Machine Learning
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This paper aims to develop a tool for predicting. v5 P7 K# s6 p* T2 u a. V
accurate and timely traffific flflow Information. Traffific Environment) m, m$ A$ g1 L! T
involves everything that can affect the traffific flflowing on the
( R& s- Y1 H# Lroad, whether it’s traffific signals, accidents, rallies, even repairing
' E0 e S. ?0 D9 G9 Oof roads that can cause a jam. If we have prior information1 K+ X4 T* _( Q5 M* {
which is very near approximate about all the above and many
/ |; Y. W5 ]/ `# y" z! hmore daily life situations which can affect traffific then, a driver" `2 G3 b& N: l [9 q% v& w
or rider can make an informed decision. Also, it helps in the
7 ^( Q% T! {8 e& [3 kfuture of autonomous vehicles. In the current decades, traffific data* n% W( g2 Q3 R' Y, V3 a0 d: A# E2 G
have been generating exponentially, and we have moved towards
) U; v! j; H5 Y* \$ c# fthe big data concepts for transportation. Available prediction
' h! J+ G1 K3 ^8 }# I+ Xmethods for traffific flflow use some traffific prediction models and
7 M) X0 x9 B6 {8 D6 D, rare still unsatisfactory to handle real-world applications. This fact, t) E9 h1 T+ G( F8 U
inspired us to work on the traffific flflow forecast problem build on
5 U3 ^( E# r$ P& s, j. m+ Dthe traffific data and models.It is cumbersome to forecast the traffific
d' l: M2 M& ^, C* U4 b; |+ Lflflow accurately because the data available for the transportation! }' [' R/ z& \3 o5 Y' e
system is insanely huge. In this work, we planned to use machine
/ U, O" d( ?: I9 _ y2 Nlearning, genetic, soft computing, and deep learning algorithms5 G0 k2 H8 M2 X# N" C; {" }
to analyse the big-data for the transportation system with; }: y: w! c0 Y5 {) n; K
much-reduced complexity. Also, Image Processing algorithms are
& d, A/ B D8 \9 |* n6 Ninvolved in traffific sign recognition, which eventually helps for the
3 B6 o. X9 b( m' U% Gright training of autonomous vehicles.
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Traffic Prediction for Intelligent Transportation.pdf
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