Journal
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Volume 14, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3385414
Keywords
Traffic prediction; CNNs; traffic flow prediction; spatio-temporal analysis
Funding
- National Key R&D Program of China [2018YFB1003401]
- National Outstanding Youth Science Program of National Natural Science Foundation of China [61625202]
- International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China [61860206011]
- National Natural Science Foundation of China [61902120]
- Postdoctoral Science Foundation of China [2019M662768, 2019TQ0086]
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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 spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.
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