基于图对比学习的多模态交通流量协同预测方法

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-model traffic 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 contrastive 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.