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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, |2 C* X7 F2 v& s
accurate and timely traffific flflow Information. Traffific Environment8 U: G1 d9 P% C2 p, f9 L, p
involves everything that can affect the traffific flflowing on the" X: }6 _1 h# z/ F
road, whether it’s traffific signals, accidents, rallies, even repairing
. p) z0 ~& v! O6 n& D- j/ lof roads that can cause a jam. If we have prior information* s0 @' O8 ~2 t4 q; f* p* Z
which is very near approximate about all the above and many+ L; V3 z) D7 H9 b# J/ K6 Q @# `
more daily life situations which can affect traffific then, a driver, [; y3 v* i8 }1 ]+ q1 y
or rider can make an informed decision. Also, it helps in the4 F8 E5 I3 X3 S% G) g
future of autonomous vehicles. In the current decades, traffific data
- g }+ {$ t6 b6 {have been generating exponentially, and we have moved towards
& H5 z' V! Y' K& S1 S7 C+ [the big data concepts for transportation. Available prediction
: v4 f% d7 p+ B* r2 Rmethods for traffific flflow use some traffific prediction models and+ O% w& {4 g* Q
are still unsatisfactory to handle real-world applications. This fact5 T3 {1 O4 z( x/ n
inspired us to work on the traffific flflow forecast problem build on$ v: }% \+ C' q; u3 v. X
the traffific data and models.It is cumbersome to forecast the traffific& ~, z& p/ m% L% x
flflow accurately because the data available for the transportation
& v( o2 U9 j% L5 A2 f6 {system is insanely huge. In this work, we planned to use machine0 M. W. F4 [- r- |
learning, genetic, soft computing, and deep learning algorithms$ t& X( L0 m5 q! }7 u
to analyse the big-data for the transportation system with
5 \- t' i4 z( Zmuch-reduced complexity. Also, Image Processing algorithms are
! I5 `: |3 q0 v* \* ~involved in traffific sign recognition, which eventually helps for the
5 ?( M) A+ \ z$ c/ Q9 h9 h8 rright training of autonomous vehicles.
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