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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
    5 H  A& g; j" @" ^; t
    System using Machine Learning

    - A9 H' {7 J% Q$ l% h$ q) ~" z# x" w) s1 [$ ?
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    . h/ H& X8 I$ j* t6 d2 ^

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    $ @' B% E7 y  w' iThis paper aims to develop a tool for predicting: o. s  {7 u9 X' v
    accurate and timely traffific flflow Information. Traffific Environment& g7 l! M# P, o
    involves everything that can affect the traffific flflowing on the" k, ]; b; e- O2 L3 ^
    road, whether it’s traffific signals, accidents, rallies, even repairing
    9 }& J( h0 [% xof roads that can cause a jam. If we have prior information$ ]4 _6 }7 p0 p) z
    which is very near approximate about all the above and many
      u( r" @3 t1 ymore daily life situations which can affect traffific then, a driver; I5 N( |2 [* \5 x4 R! j
    or rider can make an informed decision. Also, it helps in the
    & I5 S( m) z/ ?0 jfuture of autonomous vehicles. In the current decades, traffific data
    7 L$ w3 o6 i+ @" K; qhave been generating exponentially, and we have moved towards' R3 H9 p$ G- c. i, }9 h. }% A4 J
    the big data concepts for transportation. Available prediction
    : f; q3 `. c$ H8 Y5 n  Emethods for traffific flflow use some traffific prediction models and
    , |, {0 o2 J" N5 H9 P* uare still unsatisfactory to handle real-world applications. This fact
    - M/ m$ e+ b# a' {: ~1 \. `inspired us to work on the traffific flflow forecast problem build on
    7 r3 B4 m; Z9 ]* h" athe traffific data and models.It is cumbersome to forecast the traffific' F9 K. E# u$ q# t+ B# X1 k: L
    flflow accurately because the data available for the transportation
    % q: m; l6 O+ }/ I" z6 I( r8 psystem is insanely huge. In this work, we planned to use machine- {* `3 z+ ~) W
    learning, genetic, soft computing, and deep learning algorithms
    : e5 }$ A6 D* _/ j- K( zto analyse the big-data for the transportation system with9 N0 V, i# z6 i) }8 d+ Y8 L+ A
    much-reduced complexity. Also, Image Processing algorithms are
    3 b" t" [7 L9 Linvolved in traffific sign recognition, which eventually helps for the4 ?$ E& x1 f9 ?( W' P
    right training of autonomous vehicles./ B* r  }+ N$ G! S
    - k1 u4 }9 K: j0 x
    ' G6 Y4 n. F4 `* C/ B7 q5 m. N

    Traffic Prediction for Intelligent Transportation.pdf

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