Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because
of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some
work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to
explore spatial relations and temporal relations, respectively, which outperformed the classical approaches.
However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies
utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited
in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal
features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control
the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these,
we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic
flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatiotemporal
CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive
experiments on two real traffic datasets demonstrate that MGSTC outperforms other state-of-the-art
baselines.
License type:
PublisherCopyrights
Funding Info:
Grant, NRF, NRF-NSFC Joint Grant Call (NRF2016NRF-NSFC001-111)