计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 135-145.doi: 10.11896/jsjkx.230700127

• 人工智能 • 上一篇    下一篇

基于图对比学习的多模态交通流量协同预测方法

肖杨1, 秦建阳1, 李肯立2, 王鸽3, 李瑞4, 廖清1,5   

  1. 1 哈尔滨工业大学(深圳)计算机科学与技术学院 广东 深圳518055
    2 湖南大学信息科学与工程学院 长沙410082
    3 西安交通大学计算机学院 西安710049
    4 西安电子科技大学计算机科学与技术学院 西安710071
    5 鹏城实验室 广东 深圳518000
  • 收稿日期:2023-07-18 修回日期:2023-09-18 出版日期:2023-10-10 发布日期:2023-10-10
  • 通讯作者: 廖清(liaoqing@hit.edu.cn)
  • 作者简介:(curlyxiao897@163.com)
  • 基金资助:
    国家重点研发计划(2020YFB2104003)

Co-Forecasting for Multi-modal Traffic Flow Based on Graph Contrastive Learning

XIAO Yang1, QIN Jianyang1, LI Kenli2, WANG Ge3, LI Rui4, LIAO Qing1,5   

  1. 1 School of Computer Science and Technology,Harbin Institute of Technology(Shenzhen),Shenzhen,Guangdong 518055,China
    2 College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
    3 School of Computer Science and Technology,Xi'an Jiaotong University,Xi'an 710049,China
    4 School of Computer Science and Technology,Xidian University,Xi'an 710071,China
    5 Pengcheng Laboratory,Shenzhen,Guangdong 518000,China
  • Received:2023-07-18 Revised:2023-09-18 Online:2023-10-10 Published:2023-10-10
  • About author:XIAO Yang,born in 2001,postgra-duate.Her main research interests include artificial intelligence and spatio-temporal data mining.LIAO Qing,born in 1988,Ph.D,professor.Her research interests include artificial intelligence and data mining.
  • Supported by:
    National Key Research and Development Program of China(2020YFB2104003).

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摘要： 准确的城市区域交通流量预测对市区车辆调度、公交系统优化等具有重要指导意义。目前,大多数现有的交通流量预测方法只考虑规则网格区域上单一种类的交通流量预测,忽略了交通网络中空间的不规则性和异质性以及不同出行模式交通流的交互性。针对上述问题,提出了一种基于图对比学习的多模态交通流量协同预测方法(CoF-MGCL),以揭示各类出行方式之间的交互对不规则异构区域的交通需求的影响。具体而言,根据现实中城市的不规则区域采集多模态流量数据,包括各类出行模式流量(如自行车和出租车流量)和总流量;并对不规则区域构建多关系异构图,包括地理邻近和功能相似关系。通过异构图编码模块,可以结合异构图中不同的关系来学习各区域各类交通流量的高质量表征信息。学习到的单一交通流量表征经过注意力机制加权融合后与总交通流量表征进行图对比学习,以捕获不同出行模式之间的交互关系。最后,使用互信息约束实现多模态流量的协同预测,确保多模态信息学习最大化。为了实现不规则区域的多模态交通流量预测,自行构建了新的纽约市曼哈顿区和芝加哥市两地多模态交通流量数据集,并在此基础上进行实验。实验结果表明,所提方法可以结合现有的单模态交通流量预测模型,在均方误差(RMSE)和平均绝对误差(MAE)两个预测指标上实现0.43%～12.13%的性能提升,验证了所提方法的有效性。

关键词: 交通流量预测, 多模态, 异构图表示, 图对比学习, 互信息

Abstract: An accurate traffic flow prediction in urban areas is of important significance to provide guidance for urban vehicle scheduling and public transportation system optimization.So far,most existing traffic flow prediction methods only consider a single type of traffic flow prediction in a regular grid area,ignoring the spatial irregularity and heterogeneity in the traffic network and the interactivity among different kinds of traffic flow.To address these problems,this paper proposes a co-forecasting method for multi-modal traffic flow based on graph contrastive learning,named CoF-MGCL,so as to reveal the effect of the interaction among various traffic flows on the traffic demand in irregular and heterogeneous areas.Specifically,this paper collects multi-modeltraffic data,including the individual and total traffic flow of various travel types(e.g.,the traffic flow of bike and taxi);then,constructs a heterogeneous graph with multiple relations,including geographical proximity and functional similarity relations,for irregular areas.By using a heterogeneous graph coding module,this paper can fuse multiple relations in a heterogeneous graph to learn high-quality representations for various traffic flows in different areas.The learned representations of each individual traffic flow are integrated via an attention mechanism,which is compared with the representation of total traffic flow via a graph con-trastive learning,so as to capture the interactive correlation among different traffic flows.Finally,this paper introduces a mutual information regularization for multi-modal traffic flow co-forecasting,maximizing multi-modal information learning.To achieve multi-modal traffic flow forecasting in irregular areas,two new multi-modal traffic flow datasets for the Manhattan Borough of New York and Chicago have been constructed and used for experiments.Experimental results demonstrate that the proposed method can be combined with existing uni-modal traffic flow forecasting methods to obtain 0.60%~12.13% performance gains in terms of root-mean-square error(RMSE) and mean-absolute error(MAE),verifying the effectiveness of the proposed method.

Key words: Traffic flow forecasting, Multi-modal, Heterogeneous graph representation, Graph contrastive learning, Mutual information

中图分类号: 

  • TP183
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