A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting

• URL: http://arxiv.org/abs/2205.10365v1
• Date: Fri, 20 May 2022 04:38:49 GMT
• Title: A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
• Authors: Weiguo Zhu, Yongqi Sun, Xintong Yi, Yan Wang
• Abstract summary: We propose a novel correlation information-basedtemporal network (CorrSTN) for predicting traffic patterns. In particular, our model makes significant improvements compared with the latest model ASTGNN by 12.7%, 14.4% and 27.4% in the metrics of MAE, RMSE and MAPE, respectively.
• Score: 4.933291769305828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growth of transport modes, high traffic forecasting precision is required in intelligent transportation systems. Most previous works utilize the transformer architecture based on graph neural networks and attention mechanisms to discover spatiotemporal dependencies and dynamic relationships. The correlation information among spatiotemporal sequences, however, has not been thoroughly considered. In this paper, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr), among spatiotemporal sequences based on the maximal information coefficient. Using SCorr, we propose a novel correlation information-based spatiotemporal network (CorrSTN), including a dynamic graph neural network component incorporating correlation information into the spatial structure effectively and a multi-head attention component utilizing spatial correlation information to extract dynamic temporal dependencies accurately. Using TCorr, we further explore the correlation pattern among different periodic data and then propose a novel data selection scheme to identify the most relevant data. The experimental results on the highway traffic flow (PEMS07 and PEMS08) and metro crowd flow (HZME inflow and outflow) datasets demonstrate that CorrSTN outperforms the state-of-the-art methods in terms of predictive performance. In particular, on the HZME (outflow) dataset, our model makes significant improvements compared with the latest model ASTGNN by 12.7%, 14.4% and 27.4% in the metrics of MAE, RMSE and MAPE, respectively.

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