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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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) A- i2 \  ~) j0 J. hThis paper aims to develop a tool for predicting
7 o3 q. e: A" R( m! b' eaccurate and timely traffific flflow Information. Traffific Environment
4 T) {& z5 {- Q2 N9 C4 A' J1 G5 v- Ginvolves everything that can affect the traffific flflowing on the
7 B- U* n$ G9 c# W- Froad, whether it’s traffific signals, accidents, rallies, even repairing. a5 I8 j; B2 V: @! d. A, _
of roads that can cause a jam. If we have prior information7 e! A; B1 f. Q1 |7 J' @% [
which is very near approximate about all the above and many/ }# m* q* p. }7 D
more daily life situations which can affect traffific then, a driver
% o! E3 R# S3 q9 B! Uor rider can make an informed decision. Also, it helps in the- l- M2 [5 v8 P  d% r& Q
future of autonomous vehicles. In the current decades, traffific data
- B" o* @" N: L. W8 ahave been generating exponentially, and we have moved towards% K; y/ y$ j8 V4 q1 _' E" V; {& p
the big data concepts for transportation. Available prediction4 T( |: D$ H6 p* U6 T& g( {+ K3 \
methods for traffific flflow use some traffific prediction models and
  }/ ^2 }3 j) W, nare still unsatisfactory to handle real-world applications. This fact
# Z; |" L& b5 f1 o" uinspired us to work on the traffific flflow forecast problem build on$ \% J4 V- o- o/ }5 n
the traffific data and models.It is cumbersome to forecast the traffific
( H1 Z5 F! L& [# w% Z: }. |. }5 Dflflow accurately because the data available for the transportation( ^+ E) {8 v+ j/ J8 o! B
system is insanely huge. In this work, we planned to use machine
% K, v: @1 z4 Y/ e! W3 A! U  ulearning, genetic, soft computing, and deep learning algorithms1 a6 I, w  W# b& }
to analyse the big-data for the transportation system with
$ ~; F" W3 h# q0 l! b' mmuch-reduced complexity. Also, Image Processing algorithms are
5 V" q3 {" n6 ^; r! l6 d# K# Einvolved in traffific sign recognition, which eventually helps for the
0 Z! N  K- m4 O, o7 u9 b: W: Y" H. L3 {right training of autonomous vehicles.
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Traffic Prediction for Intelligent Transportation.pdf

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