中山大学学报自然科学版 ›› 2018, Vol. 57 ›› Issue (6): 88-96.doi: 10.13471/j.cnki.acta.snus.2018.06.011

• 论文 • 上一篇    下一篇

基于多类型浮动车数据的高速公路路段速度修正模型

孙威巍1,2,何兆成1,2,陈锐祥1,2,叶伟佳1,2   

  1. 1. 中山大学智能交通研究中心,广东 广州 510006;
    2. 广东省智能交通系统重点实验室,广东 广州 510006
  • 收稿日期:2018-03-20 出版日期:2018-11-25 发布日期:2018-11-25
  • 通讯作者: 何兆成(1977年生), 男;研究方向:交通信息处理;E-mail:hezhch@mail.sysu.edu.cn

Expressway link speed correction model based on multiple types of floating car data

SUN Weiwei1,2, HE Zhaocheng1,2, CHEN Ruixiang1,2, YE Weijia1,2     

  1. 1. Research Center of Intelligent Transport System, Sun Yat-sen University, Guangzhou 510006, China;
    2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510006, China
  • Received:2018-03-20 Online:2018-11-25 Published:2018-11-25

摘要:

考虑到存在多类型浮动车,且不同车型之间车辆性能等不同,为了获得更加准确的路段速度,本文区分车辆类型,使用人工神经网络技术对浮动车速度和高速公路路段速度进行了建模。利用广州机场高速上的浮动车数据进行验证,并与基于贝叶斯网络的方法进行比较。结果表明:修正前速度的平均绝对相对误差(MAPE)约为20%,平均绝对误差(ABS)约为8 km/h,修正后速度的平均绝对相对误差在10%以内,平均绝对误差在5 km/h以内,说明该方法具有较好的效果。

关键词: 多类型浮动车数据, 路段速度修正, 人工神经网络, 高速公路路段速度

Abstract:

Taking into account the multiple types of floating cars and the different vehicles’ performance among distinct vehicle types, in order to obtain more accurate link speed, this paper distinguishes vehicle types and introduces artificial neural network technology to model the velocity of the floating vehicles and link speeds on highway. We apply the floating car data from Guangzhou Airport highway for model verification, and compared with the Bayesian Network based method. The results show that the average absolute relative error (MAPE) of the link speed before correction is about 20%, the average absolute error (ABS) is about 8 km/h, the MAPE of the link speed after correction is less than 10%, and the ABS is less than 5 km/h, which shows that the proposed method has good effect.

 

Key words: multiple types of floating car data;road speed correction, artificial neural network, road speed of highway

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