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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' o$ J9 y1 q, X% m" {7 p% p; i
accurate and timely traffific flflow Information. Traffific Environment
/ Y: C8 G% S! D3 ^* k* iinvolves everything that can affect the traffific flflowing on the& \% h2 n7 `4 }
road, whether it’s traffific signals, accidents, rallies, even repairing
% J) R' d- h& ?0 U- B# V4 p# Nof roads that can cause a jam. If we have prior information
) K9 [+ H( [" f6 iwhich is very near approximate about all the above and many
+ h7 P5 a/ [7 X1 Fmore daily life situations which can affect traffific then, a driver" H. j, ]5 |2 ?
or rider can make an informed decision. Also, it helps in the* ]  U+ P: p* }  n' o2 i! g6 C# }, x, U
future of autonomous vehicles. In the current decades, traffific data
# e- d& U. H/ k4 p" n5 _have been generating exponentially, and we have moved towards
0 S, K; M; q3 r% d5 Tthe big data concepts for transportation. Available prediction
  ~) {+ @1 T- s7 `$ \methods for traffific flflow use some traffific prediction models and6 j% E" k: H+ D) t8 o
are still unsatisfactory to handle real-world applications. This fact+ o; |5 m- g& ]5 k+ i
inspired us to work on the traffific flflow forecast problem build on  {3 o8 ]% }* `, _2 L! r6 m5 S* s; T
the traffific data and models.It is cumbersome to forecast the traffific
1 q$ ]  [% G- cflflow accurately because the data available for the transportation
% ^7 T, Q3 D" @' R: D7 Q/ y" H  asystem is insanely huge. In this work, we planned to use machine
! c1 X( {0 B* p" E. K) Z, ]5 llearning, genetic, soft computing, and deep learning algorithms
8 f# i0 Y  s0 M0 d$ a" k5 Y# K. \to analyse the big-data for the transportation system with
# E- {- W; c9 N) amuch-reduced complexity. Also, Image Processing algorithms are
- s7 p( I7 R- {& z8 v3 Iinvolved in traffific sign recognition, which eventually helps for the
- l% s: w+ W1 ]4 h4 `right training of autonomous vehicles.
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Traffic Prediction for Intelligent Transportation.pdf

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