数学建模社区-数学中国

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

4 z. T: T! Z" a. G
System using Machine Learning
& S7 Z$ x0 n+ F3 R0 {' P) }
( S2 b) L0 P) [$ ~( @1 k+ Q2 |8 D
) n# G. f* {! |4 h3 i! ^0 d& P2 U
% e8 n. q/ _9 V% o5 n0 j
3 S$ R9 O: G3 F0 E: X8 R- C
This paper aims to develop a tool for predicting  M7 d/ {4 m# H# d, j' T
accurate and timely traffific flflow Information. Traffific Environment
5 C$ X! ^+ f% J4 {involves everything that can affect the traffific flflowing on the
5 Q. _/ O3 t9 J' \/ I0 kroad, whether it’s traffific signals, accidents, rallies, even repairing" J7 }9 b; H& L) [/ P) W
of roads that can cause a jam. If we have prior information! G! ~0 @1 g7 G. E4 I0 Y- V/ M  z
which is very near approximate about all the above and many' r! }3 A& i! w- }4 a
more daily life situations which can affect traffific then, a driver
4 b7 i7 U* X4 O- vor rider can make an informed decision. Also, it helps in the% o6 Q, I* z+ k2 `0 o; c
future of autonomous vehicles. In the current decades, traffific data
7 `  o; J; C( P, K" `$ uhave been generating exponentially, and we have moved towards
. d6 U; H) @: U$ i1 O( \% U/ _the big data concepts for transportation. Available prediction
5 f1 N! J; q  |# D) N: tmethods for traffific flflow use some traffific prediction models and
( ?7 a2 a; _/ h  z, qare still unsatisfactory to handle real-world applications. This fact
8 q4 F: M$ d; t& t' }inspired us to work on the traffific flflow forecast problem build on
. K- I+ z) ]# |4 [  g, G' xthe traffific data and models.It is cumbersome to forecast the traffific3 q0 Q/ [* F2 a5 _/ O& u2 a3 u
flflow accurately because the data available for the transportation! m, N8 ?9 N2 j9 N& o9 S$ b8 B" r
system is insanely huge. In this work, we planned to use machine: ?1 D4 b# |+ p) v" O7 R5 v
learning, genetic, soft computing, and deep learning algorithms
/ [& A% R- v6 uto analyse the big-data for the transportation system with
+ x7 m+ W( V! b/ x. Omuch-reduced complexity. Also, Image Processing algorithms are
' _, c* c" w: T* b' J3 f6 U8 W! Xinvolved in traffific sign recognition, which eventually helps for the
4 J; H4 e- j  J! V% ?- Iright training of autonomous vehicles.4 E# R4 V7 Y, c/ T7 u- K' {

2 n# @9 R$ ^! B/ _9 @' X
8 @) R  d' y* l0 `: Y# B  `% }' l0 I& J$ _- p0 Q

6 I4 c& _! M8 g( X. j2 }7 g$ I2 U: p' v0 b& K

09091758.pdf

425.77 KB, 下载次数: 1, 下载积分: 体力 -2 点



欢迎光临 数学建模社区-数学中国 (http://www.madio.net/) Powered by Discuz! X2.5