中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2021, Vol. 40 ›› Issue (11): 60-66.DOI: 10.3969/j.issn.1674-0696.2021.11.09

• 交通+大数据人工智能 • 上一篇    

综合小波分解和BP神经网络的交通小区生成交通短时预测

赵顗,沈玲宏,马健霄,邱烜利   

  1. (南京林业大学 汽车与交通工程学院,江苏 南京 210037)
  • 收稿日期:2020-06-09 修回日期:2020-10-12 发布日期:2021-11-24
  • 作者简介:赵顗(1989—),男,安徽池州人,讲师,博士,主要从事交通运输规划与管理方面的研究。E-mail:zhaoyi207@126.com 通信作者:沈玲宏(1995—),女,江苏丹阳人,硕士研究生,主要从事交通运输规划与管理方面的研究。E-mail:1547193251@qq.com
  • 基金资助:
    江苏省高校自然科学研究面上项目(19KJB580012)

Traffic Short-Term Prediction Generated by Wavelet Decomposition and BP Neural Network of Traffic Zone

ZHAO Yi, SHEN Linghong, MA Jianxiao, QIU Xuanli   

  1. (School of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China)
  • Received:2020-06-09 Revised:2020-10-12 Published:2021-11-24

摘要: 针对交通小区生成交通的短时预测需求,提出了综合小波分析和BP神经网络的短时预测方法。预测方法主要利用dbN小波函数对交通小区生成交通进行小波分解,利用BP神经网络对分解后的多频段波形进行短时预测,最后通过波形重构获得交通小区生成交通的短时预测结果。在构建综合小波分析和BP神经网络短时预测模型基础上,采集交通小区的实际交通生成数据,并构建短时预测的对比模型,检验构建模型的预测精度。检验结果表明:在交通小区的生成交通短时预测方面,综合小波分析和BP神经网络的组合预测模型比单独采用BP神经网络进行预测的精度更高。

关键词: 交通工程;短时预测;小波分析;BP神经网络;生成交通

Abstract: A short-term prediction method based on wavelet analysis and BP neural network was proposed to meet the short-term prediction demand of traffic zone’s generated traffic. The dbN wavelet function was used by the proposed prediction method to decompose the traffic zone’s generated traffic. And the BP neural network was used to predict each waveform with different frequency bands after decomposition in a short time. Finally, the short-term prediction results of the traffic zone’s generated traffic can be obtained by waveform reconstruction. On the basis of constructing the short-term prediction model of comprehensive wavelet analysis and BP neural network, some actually generated traffic data of the traffic zone were collected, and a comparison model of short-term prediction was constructed to test the prediction accuracy of the proposed model. The test results show that the combined prediction model of wavelet analysis and BP neural network has higher accuracy than BP neural network does alone in predicting the short-term generated traffic of the traffic zone.

Key words: traffic engineering; short-term prediction; wavelet analysis; BP neural network; generated traffic

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