Spatiotemporal Residual Graph Attention Network for Traffic Flow Forecasting | IEEE Journals & Magazine | IEEE Xplore

Spatiotemporal Residual Graph Attention Network for Traffic Flow Forecasting


Abstract:

Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatiotemporal characteristics of t...Show More

Abstract:

Accurate spatiotemporal traffic flow forecasting is significant for the modern traffic management and control. In order to capture the spatiotemporal characteristics of the traffic flow simultaneously, we propose a novel spatiotemporal residual graph attention network (STRGAT). First, the network adopts a deep full residual graph attention block, which performs a dynamic aggregation of spatial features regarding the node information of the traffic network. Second, a sequence-to-sequence block is designed to capture the temporal dependence in the traffic flow. The traffic flow data with weekly periodic dependencies are also integrated and STRGAT is used for traffic forecasting of traffic road networks. The experiments are conducted on three real data sets in California, USA. Results verify that our proposed STRGAT is able to learn the spatiotemporal correlation of traffic flow well and outperforms the state-of-the-art methods.
Published in: IEEE Internet of Things Journal ( Volume: 10, Issue: 13, 01 July 2023)
Page(s): 11518 - 11532
Date of Publication: 07 February 2023

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