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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

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    System using Machine Learning

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    This paper aims to develop a tool for predicting3 Y& N: k) O0 _' l/ }2 i1 N
    accurate and timely traffific flflow Information. Traffific Environment5 [5 s& e, y% e9 W4 w! F
    involves everything that can affect the traffific flflowing on the
    " I3 z0 e# Q3 j* L- J" S3 yroad, whether it’s traffific signals, accidents, rallies, even repairing/ [+ [3 u" [! C( A% B
    of roads that can cause a jam. If we have prior information
    8 B6 y* W: o3 l$ `; x0 Xwhich is very near approximate about all the above and many
    # o( k( P* |8 |more daily life situations which can affect traffific then, a driver
    * |2 ?2 [9 o& Qor rider can make an informed decision. Also, it helps in the+ _: U: b: ^5 b
    future of autonomous vehicles. In the current decades, traffific data5 D$ T! a; a4 Y" I$ @4 b" d: m
    have been generating exponentially, and we have moved towards1 \' R& N# k9 g1 Q# m
    the big data concepts for transportation. Available prediction
    ) h5 s) t4 d7 Y" Q% Nmethods for traffific flflow use some traffific prediction models and
    & e, Z- ?4 N. y; m; u* care still unsatisfactory to handle real-world applications. This fact5 N0 ?& W! `1 A: X2 g) ~, O. b7 d" l
    inspired us to work on the traffific flflow forecast problem build on
    ; W9 p) v9 q  X7 n  E1 @8 Jthe traffific data and models.It is cumbersome to forecast the traffific
    0 h+ K/ R7 ^: ^8 l$ v0 \  Tflflow accurately because the data available for the transportation
    : @* h2 T3 z+ b8 A: `; L$ q# M6 qsystem is insanely huge. In this work, we planned to use machine  m1 P' d9 S& K) `3 o5 N3 U# ^
    learning, genetic, soft computing, and deep learning algorithms
    3 g3 t1 J. O" Z9 \5 z1 G8 Eto analyse the big-data for the transportation system with' n# l8 F6 d7 f/ q# c# Q1 e6 v
    much-reduced complexity. Also, Image Processing algorithms are
    % i' H! q* Z% H; ]# s" }involved in traffific sign recognition, which eventually helps for the
    2 Z( i* Y8 Y6 w2 N# |) i% X. `right training of autonomous vehicles.
    3 g! _+ ^" c  X
    & u7 R1 A$ P3 m. h4 t  w8 A9 ^) V' B# M

    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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