中山大学学报自然科学版 ›› 2019, Vol. 58 ›› Issue (6): 111-118.doi: 10.13471/j.cnki.acta.snus.2019.06.014

• 论文 • 上一篇    下一篇

基于局部加权线性回归的城市公交车排放能耗预测

杨鹏史1,2,3,丁卉1,2,3,陈同1,2,3,刘永红1,2,3   

  1. 1.中山大学智能工程学院, 广东 广州 510006;
    2.广东省交通环境智能监测与治理工程技术研究中心, 广东 广州 510275;
    3.广东省智能交通系统重点实验室,广东 广州510275
  • 收稿日期:2018-11-22 出版日期:2019-11-25 发布日期:2019-11-25
  • 通讯作者: 刘永红(1977年生),女;研究方向:交通环境大数据及尾气污染控制研究;E-mail: liu_its@163.com

Estimation of emissions or electricity consumptions of urban buses based on Locally Weighted Linear Regression

YANG Pengshi1,2,3, DING Hui1,2,3, CHENG Tong1,2,3, LIU Yonghong1,2,3   

  1. 1. School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China;
    2. Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control, Guangzhou 510275,China;
    3. Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510275, China
  • Received:2018-11-22 Online:2019-11-25 Published:2019-11-25

摘要:

交通运行及减排管控措施正朝动态、精细化方向发展。采集来自于交通实时平台的动态社会车辆速度数据,结合公交车实测GPS数据,应用局部加权线性回归预测了广州市中心城区部分公交车的路段平均速度和运行模式分布;并建立了面向液化天然气公交车和纯电动公交车的排放因子、耗电因子预测模型。结果表明:①公交车与社会车辆的平均速度呈正相关;②不同平均速度下公交车运行模式分布规律明显。随着公交车平均速度增加,怠速模式Bin1频率逐渐下降,减速模式Bin11~12频率先增后减,而代表低速、低加速度的Bin13~15频率逐渐向代表高速、高加速度的Bin16~17转移;③提出的算法能捕捉到公交车的平均速度随社会车辆平均速度的增加而增加的非线性趋势,公交车的平均速度预测误差为1985%;④公交车的NOx 排放因子和耗电因子的预测误差分别为20.27%和26.52%。

关键词: 公交车, 社会车辆, 运行模式, 排放因子, 耗电因子

Abstract:

Measures of controlling traffic operation and emissions are developing to be dynamic and detailed. We applied the dynamic speed data of social vehicles from traffic real-time platform and GPS data of buses from practical tests, then used LWLR (Locally Weighted Linear Regression) to forecast average speeds and operating mode distributions of some urban buses in Guangzhou. Finally a model for estimating emission factors (for liquefied natural gas buses) and electricity consumptions (for electric buses) was built. The results showed that, the average speeds of buses were positively correlated with those of social vehicles. The operating mode distributions under different average speeds had regular patterns. With the increase of the average speeds of buses, the frequency of Bin1 (idling mode) decreased, the frequencies of Bin11-12 increased firstly and then decreased, and the frequencies of Bin13-15 (denoted low speed and acceleration) were partly transformed to those of Bin16-17 (denoted high speed and acceleration). The proposed algorithm reflected the no-linear trend that the speeds of buses increased with that of social vehicles, and the mean absolute percentage error of speeds of buses was 1985%. The mean absolute percentage errors of NOx emission factors and electricity consumptions of buses were 20.27% and 26.52%.

Key words: bus, social vehicle, operating mode, emission factor, electricity consumption

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