北京大学学报自然科学版 ›› 2017, Vol. 53 ›› Issue (4): 775-782.DOI: 10.13209/j.0479-8023.2017.040

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基于时空权重相关性的交通流大数据预测方法

李欣1,2, 罗庆1,2, 孟德友1,2   

  1. 1. 河南财经政法大学中原经济区“三化”协调发展河南省协同创新中心, 郑州 450046;
    2. 河南财经政法大学资源与环境学院, 郑州 450046; † E-mail: lixin992319@163.com;
  • 收稿日期:2016-04-05 修回日期:2016-10-23 出版日期:2017-07-20 发布日期:2017-07-20
  • 基金资助:
    国家自然科学基金(41501178)和河南财经政法大学博士科研启动基金(800257)资助

Traffic Flow-Big Data Forecasting Method Based on Spatial-Temporal Weight Correlation

Xin LI1,2, Qing LUO1,2, Deyou MENG1,2   

  1. 1. Collaborative Innovation Center of Three-Aspect Coordination of Central Plain Economic Region, Henan University of Economics and Law, Zhengzhou 450046
    2. College of Resource and Environment, Henan University of Economics and Law, Zhengzhou 450046;
    † E-mail: lixin992319@163.com;
  • Received:2016-04-05 Revised:2016-10-23 Online:2017-07-20 Published:2017-07-20

摘要:

将分布式增量大数据聚合方法与交通流数据清洗规则相结合, 可以为交通流预测分析提供更准确可靠的数据源。通过交通流在路网中的相关性分析, 使用多阶路口转弯率构建空间权重矩阵, 完成对STARIMA交通流预测模型的改进。实验结果表明, 该方法可以在工作效率及准确程度上满足交通流大数据预测的需求, 为交通诱导信息发布提供依据。

关键词: 交通流, 大数据, 分布式增量, 路网相关性, STARIMA

Abstract:

A distributed incremental aggregation method combined with traffic flow data cleansing rules is proposed, and it can provide more accurate and reliable data for traffic flow forecast analysis. Through the correlation analysis of traffic flow in road network, the authors used the multi-allocation of turning rate in the intersection to build the spatial weight matrix, and improved the STARIMA traffic flow forecasting model. The experiment result proves that this method can meet the needs of traffic flow big-data forecasting in the efficiency and accuracy, and provide the basis for the traffic routing information.

Key words: traffic flow, big-data, distributed incremental, road network correlation, STARIMA