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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: 2020-11-10 16:06
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
* D; j, Z0 S4 P' I- J  S  }accurate and timely traffific flflow Information. Traffific Environment7 Y6 C  T: K, F8 ?( Z8 J
involves everything that can affect the traffific flflowing on the: ?1 c$ o0 M+ L' K  j
road, whether it’s traffific signals, accidents, rallies, even repairing
1 O! Y4 K6 i( @5 cof roads that can cause a jam. If we have prior information
1 K7 b/ t5 Q* T( S- @# _- Awhich is very near approximate about all the above and many& l4 l9 r) Y; w9 m/ J
more daily life situations which can affect traffific then, a driver
9 l% D9 x  P- nor rider can make an informed decision. Also, it helps in the+ `$ s' K4 s& m( M
future of autonomous vehicles. In the current decades, traffific data
+ L% s; k! x5 M/ |have been generating exponentially, and we have moved towards
9 {4 n: E  m5 m% l  E* q+ c4 \the big data concepts for transportation. Available prediction; f3 f8 u, y& ^7 o0 x8 I) T
methods for traffific flflow use some traffific prediction models and1 I* z2 X. j1 V) ~0 b
are still unsatisfactory to handle real-world applications. This fact
2 G& r: m' B: W- M+ Kinspired us to work on the traffific flflow forecast problem build on
# j; S- P4 q: pthe traffific data and models.It is cumbersome to forecast the traffific
" D# b' t! K3 W+ E! k0 q& ?5 lflflow accurately because the data available for the transportation: S0 z1 Q+ `: N- P/ Y- w
system is insanely huge. In this work, we planned to use machine3 `0 @" L0 e, I# S& F8 ]8 l
learning, genetic, soft computing, and deep learning algorithms
1 S, z. u# y0 I# t/ z2 Kto analyse the big-data for the transportation system with0 b1 S# P; ]. x2 ?
much-reduced complexity. Also, Image Processing algorithms are
+ N6 l: S2 @6 z$ I6 V) oinvolved in traffific sign recognition, which eventually helps for the
: R5 |+ @9 B6 O/ J+ V. }! _right training of autonomous vehicles.4 f9 \2 D# m' {2 J, T! m
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