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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
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    Traffific Prediction for Intelligent Transportation
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    System using Machine Learning
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    3 E. D4 |3 e( R1 d. |

    9 {8 n  z$ O: p4 R7 Y  }9 Z* \: L+ |

    ! D2 J3 ~& a( l0 XThis paper aims to develop a tool for predicting
    ( E9 c" I8 p0 Jaccurate and timely traffific flflow Information. Traffific Environment1 b0 h0 A* T& j* M
    involves everything that can affect the traffific flflowing on the) V3 S0 R3 G6 I) A; D9 T  d8 N
    road, whether it’s traffific signals, accidents, rallies, even repairing6 B7 \; \' E# t; z, v; U. {( `
    of roads that can cause a jam. If we have prior information6 R% E/ ]4 w1 q1 L  v
    which is very near approximate about all the above and many
    7 r+ n; G* q3 o2 s( x# imore daily life situations which can affect traffific then, a driver
    0 q* D& m# \' _2 ^  A- cor rider can make an informed decision. Also, it helps in the# }2 a% r4 K( O# S4 j: c5 ]6 m
    future of autonomous vehicles. In the current decades, traffific data
    2 {3 p. U- [% C) K( l5 m: ]" u: Zhave been generating exponentially, and we have moved towards4 q' U4 i+ k- c, Q
    the big data concepts for transportation. Available prediction, ?9 A0 C8 i5 d* q+ d
    methods for traffific flflow use some traffific prediction models and
    # k# ~- [; @* O0 q  j% M6 pare still unsatisfactory to handle real-world applications. This fact, ?! o. Q! G7 M0 n
    inspired us to work on the traffific flflow forecast problem build on
    & ^8 Y7 P/ ?( N2 M$ o3 [4 {/ Wthe traffific data and models.It is cumbersome to forecast the traffific
    ( i4 [/ |+ u, V$ M7 Mflflow accurately because the data available for the transportation+ G4 @% y3 O$ m7 S3 G8 f( s
    system is insanely huge. In this work, we planned to use machine
    * F! H' I4 ]* Q+ |! @6 olearning, genetic, soft computing, and deep learning algorithms
    1 `- x+ \! U8 M- Eto analyse the big-data for the transportation system with
    $ r/ j8 {& T3 a% u# smuch-reduced complexity. Also, Image Processing algorithms are
    # k; _. y+ a6 L7 R8 K; c5 Hinvolved in traffific sign recognition, which eventually helps for the
    5 }$ G( i$ ]& H! K8 d7 ~right training of autonomous vehicles.
    / c; W$ s$ G, m$ t+ w7 P* U9 Z& p# @; g- Y8 X/ c
    2 E2 V) Z/ E( k2 ~: c5 G4 Q; H

    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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