QQ登录

只需要一步,快速开始

 注册地址  找回密码
查看: 935|回复: 0
打印 上一主题 下一主题

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

[复制链接]
字体大小: 正常 放大
杨利霞        

5273

主题

82

听众

17万

积分

  • TA的每日心情
    开心
    2021-8-11 17:59
  • 签到天数: 17 天

    [LV.4]偶尔看看III

    网络挑战赛参赛者

    网络挑战赛参赛者

    自我介绍
    本人女,毕业于内蒙古科技大学,担任文职专业,毕业专业英语。

    群组2018美赛大象算法课程

    群组2018美赛护航培训课程

    群组2019年 数学中国站长建

    群组2019年数据分析师课程

    群组2018年大象老师国赛优

    跳转到指定楼层
    1#
    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

    ' {$ H2 }4 R  v6 G
    System using Machine Learning
    " a( p7 i1 j$ D/ u

    6 L3 n0 u- C4 g0 M6 V- e, D
    3 P: U0 [* j) p0 i
    7 v: k4 |. k" E/ S  C' T9 w# a9 L- S% a

    2 _9 M: D0 T/ a4 N+ N! O. M; M/ tThis paper aims to develop a tool for predicting
    ( k$ ]( H% S% @1 Qaccurate and timely traffific flflow Information. Traffific Environment! M# q6 e% w3 f# v* u+ j
    involves everything that can affect the traffific flflowing on the, g8 G9 t6 s$ F' J+ z, c3 U
    road, whether it’s traffific signals, accidents, rallies, even repairing/ f# B) D9 \1 W' j; \' w
    of roads that can cause a jam. If we have prior information3 T; S2 w" ]% [( |9 y, L
    which is very near approximate about all the above and many
    , u. Z3 o, M2 F" mmore daily life situations which can affect traffific then, a driver5 M3 Q) U" Y4 f5 p( T( w# n
    or rider can make an informed decision. Also, it helps in the# W" x# R: r  G4 T
    future of autonomous vehicles. In the current decades, traffific data4 ~9 s" M" ~3 z9 `, q
    have been generating exponentially, and we have moved towards
    / ^+ j9 p/ G; p% z" o- D% Pthe big data concepts for transportation. Available prediction7 a/ g7 [: ~: |
    methods for traffific flflow use some traffific prediction models and
    4 F; a8 Z- O$ L; \0 mare still unsatisfactory to handle real-world applications. This fact
    0 L1 c; |# c3 X% ?5 Iinspired us to work on the traffific flflow forecast problem build on
    , G6 c+ R3 L' A6 Pthe traffific data and models.It is cumbersome to forecast the traffific
    / u: Q' w$ o) cflflow accurately because the data available for the transportation
    6 f7 ?* u, J  h0 ?8 Xsystem is insanely huge. In this work, we planned to use machine; k/ @+ G3 u1 I
    learning, genetic, soft computing, and deep learning algorithms
    5 U+ w; W" J0 D" {9 nto analyse the big-data for the transportation system with
    ! Y. v0 k$ m  o* O3 `' K" tmuch-reduced complexity. Also, Image Processing algorithms are9 G% x+ v, q1 M$ ^/ ^, ^3 \' _2 `
    involved in traffific sign recognition, which eventually helps for the' L7 ?  C7 L* l) G6 g
    right training of autonomous vehicles.
    $ }3 Q8 ^! H4 @
    ; R- B% b0 ]5 t  ~* |0 O3 ^, i1 i4 N

    Traffic Prediction for Intelligent Transportation.pdf

    425.85 KB, 下载次数: 2, 下载积分: 体力 -2 点

    zan
    转播转播0 分享淘帖0 分享分享0 收藏收藏0 支持支持0 反对反对0 微信微信
    您需要登录后才可以回帖 登录 | 注册地址

    qq
    收缩
    • 电话咨询

    • 04714969085
    fastpost

    关于我们| 联系我们| 诚征英才| 对外合作| 产品服务| QQ

    手机版|Archiver| |繁體中文 手机客户端  

    蒙公网安备 15010502000194号

    Powered by Discuz! X2.5   © 2001-2013 数学建模网-数学中国 ( 蒙ICP备14002410号-3 蒙BBS备-0002号 )     论坛法律顾问:王兆丰

    GMT+8, 2026-6-14 22:05 , Processed in 0.653646 second(s), 54 queries .

    回顶部