Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction


  • Huaxiu Yao Pennsylvania State University
  • Xianfeng Tang Pennsylvania State University
  • Hua Wei Pennsylvania State University
  • Guanjie Zheng Pennsylvania State University
  • Zhenhui Li Pennsylvania State University



Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.




How to Cite

Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2019). Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5668-5675.



AAAI Technical Track: Machine Learning