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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 predicting2 u( A! g/ ~3 [/ Y) z
accurate and timely traffific flflow Information. Traffific Environment! V) c4 `" {1 @( s
involves everything that can affect the traffific flflowing on the
* M4 i: [* E  d7 U' o3 Jroad, whether it’s traffific signals, accidents, rallies, even repairing3 {4 h* v+ l: }* D/ T7 H# V
of roads that can cause a jam. If we have prior information4 M: ~, o" r. ^: u
which is very near approximate about all the above and many
1 F. `( ?# `; ]4 Z  nmore daily life situations which can affect traffific then, a driver/ ?9 J  }* n& Z  d) \: Z
or rider can make an informed decision. Also, it helps in the
8 K0 P( D, J* K. ~( j( dfuture of autonomous vehicles. In the current decades, traffific data* j2 ?; `1 N4 L3 E8 F# n) R
have been generating exponentially, and we have moved towards
: B/ l7 q% V8 J8 Cthe big data concepts for transportation. Available prediction+ b  i  G# z8 r, P# D  F1 b
methods for traffific flflow use some traffific prediction models and
0 ~6 J2 H9 _# q( o5 d! rare still unsatisfactory to handle real-world applications. This fact
- h( S; J% t* D) m7 Y3 p5 ginspired us to work on the traffific flflow forecast problem build on
1 X3 R: o, v& O4 h$ ]9 l! i$ u5 Rthe traffific data and models.It is cumbersome to forecast the traffific; N3 s4 d5 g" T. B1 ^
flflow accurately because the data available for the transportation
/ g8 Z+ L, X/ jsystem is insanely huge. In this work, we planned to use machine& g! h0 i. g3 m6 j) k) a
learning, genetic, soft computing, and deep learning algorithms
0 Z' V& j5 O1 G. j, Ato analyse the big-data for the transportation system with
$ j: r, v+ |% P& U. Cmuch-reduced complexity. Also, Image Processing algorithms are
1 }* x/ q- e; Q' jinvolved in traffific sign recognition, which eventually helps for the
$ o. o9 ]7 R1 T4 U. y$ Qright training of autonomous vehicles.' q1 H: v9 Z# |7 X
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Traffic Prediction for Intelligent Transportation.pdf

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