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[其他资源] Traffic Prediction for Intelligent Transportation System using Machine Learning

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杨利霞        

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    2021-8-11 17:59
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    发表于 2020-11-12 16:27 |只看该作者 |倒序浏览
    |招呼Ta 关注Ta
    Traffific Prediction for Intelligent Transportation
    0 d+ D9 n% \& R/ g) W& U
    System using Machine Learning
      K0 K* W) k; [3 n1 v3 P' L

    3 B: K6 L* h$ Z7 o: t. b+ p' R) K) c& e

    ! F% c5 G& S2 w( P9 A. S- d- U% N6 i! w" p* `
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    This paper aims to develop a tool for predicting) U# n0 j& @! C! I# k' h2 V; S8 P2 r
    accurate and timely traffific flflow Information. Traffific Environment  e2 I" l& y+ W/ S  Y
    involves everything that can affect the traffific flflowing on the$ G4 [& Z4 c1 y1 _5 H
    road, whether it’s traffific signals, accidents, rallies, even repairing
    ( N% ]1 g7 e3 V0 ~! Eof roads that can cause a jam. If we have prior information
    , Z& J0 O7 ]3 }# a4 u! Kwhich is very near approximate about all the above and many( F% x+ q# R; {( U
    more daily life situations which can affect traffific then, a driver, N# M0 @- r& e
    or rider can make an informed decision. Also, it helps in the
    1 @) Y: E  r. M* Y% y$ h6 o, dfuture of autonomous vehicles. In the current decades, traffific data, G4 ]; J4 l* G& X& t7 T1 l
    have been generating exponentially, and we have moved towards
    % @( i! t/ E1 G; W7 o7 Lthe big data concepts for transportation. Available prediction* g& i" G2 g) s" x- b# N. @; S
    methods for traffific flflow use some traffific prediction models and
    0 j( }" F0 P- u. a$ |* e( Dare still unsatisfactory to handle real-world applications. This fact
    $ N9 W8 E$ S  N) M' Iinspired us to work on the traffific flflow forecast problem build on
    9 `) t- _* e$ ]the traffific data and models.It is cumbersome to forecast the traffific
    & C$ s9 I% L- m$ hflflow accurately because the data available for the transportation! F  s- L6 ?% x
    system is insanely huge. In this work, we planned to use machine
    5 y* h* P0 U0 i% W( M8 wlearning, genetic, soft computing, and deep learning algorithms, P( G( o4 ]+ c" v
    to analyse the big-data for the transportation system with
    7 [" `2 t8 x' W1 tmuch-reduced complexity. Also, Image Processing algorithms are  \! ?+ K1 l0 m
    involved in traffific sign recognition, which eventually helps for the& t9 j& V' o; g" k( ~/ y3 B! C
    right training of autonomous vehicles.
    1 t& j1 I2 \  ~
    " U) t( |4 n. W' y) \
    ( f- }- H. _- @  W" C

    Traffic Prediction for Intelligent Transportation.pdf

    425.85 KB, 下载次数: 2, 下载积分: 体力 -2 点

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