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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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/ w$ Y$ U" k0 M4 T( D5 j$ jThis paper aims to develop a tool for predicting( V6 t: x; \$ t7 G+ E* l
accurate and timely traffific flflow Information. Traffific Environment& Z8 n; b# G& G, I' c& w4 _
involves everything that can affect the traffific flflowing on the
F, ^. K8 ?) b& lroad, whether it’s traffific signals, accidents, rallies, even repairing/ p4 W# H/ ?1 l) x9 r1 t$ {
of roads that can cause a jam. If we have prior information
7 q5 e5 J; T; I6 o6 [which is very near approximate about all the above and many
! O, ]* u% R5 g4 g5 k: z+ T0 Qmore daily life situations which can affect traffific then, a driver
+ F2 t' \3 j9 D; W( G) Oor rider can make an informed decision. Also, it helps in the* k- m: Z/ j; j6 k
future of autonomous vehicles. In the current decades, traffific data
' F8 j+ t3 Y, L8 G3 V5 shave been generating exponentially, and we have moved towards; a. g: c: Q) E0 l: U6 i
the big data concepts for transportation. Available prediction) r8 m! d: G* C2 ]* _& Z/ x
methods for traffific flflow use some traffific prediction models and
2 a5 X1 L- l/ o2 w( p" C2 care still unsatisfactory to handle real-world applications. This fact
9 h* f, Z, A, h) qinspired us to work on the traffific flflow forecast problem build on l# A/ L3 E; E
the traffific data and models.It is cumbersome to forecast the traffific4 v& t- v6 R/ i; o
flflow accurately because the data available for the transportation! R D5 m4 j1 S- J. H' ~4 } }
system is insanely huge. In this work, we planned to use machine- I" V8 w6 G% n( \( }( p
learning, genetic, soft computing, and deep learning algorithms
3 t. z+ h$ e4 g, A' Gto analyse the big-data for the transportation system with- U# C' H8 w( y/ J+ [# h
much-reduced complexity. Also, Image Processing algorithms are
, X/ G: J2 V" V: k/ einvolved in traffific sign recognition, which eventually helps for the
4 Y4 X4 C: F T! xright training of autonomous vehicles.& S8 k V3 t: _7 G8 I9 E
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Traffic Prediction for Intelligent Transportation.pdf
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