PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation
  gating for fine-grained crowd flow prediction
Chung Park Junui Hong Cheonbok Park Taesan Kim Minsung Choi Jaegul Choo
Understanding the movement patterns of objects (e.g., humans and vehicles) in
a city is essential for many applications, including city planning and
management. This paper proposes a method for predicting future city-wide crowd
flows by modeling the spatio-temporal patterns of historical crowd flows in
fine-grained city-wide maps. We introduce a novel neural network named PArallel
Spatio-Temporal Attention with spatial auto-correlation gating (PASTA) that
effectively captures the irregular spatio-temporal patterns of fine-grained
maps. The novel components in our approach include spatial auto-correlation
gating, multi-scale residual block, and temporal attention gating module. The
spatial auto-correlation gating employs the concept of spatial statistics to
identify irregular spatial regions. The multi-scale residual block is
responsible for handling multiple range spatial dependencies in the
fine-grained map, and the temporal attention gating filters out irrelevant
temporal information for the prediction. The experimental results demonstrate
that our model outperforms other competing baselines, especially under
challenging conditions that contain irregular spatial regions. We also provide
a qualitative analysis to derive the critical time information where our model
assigns high attention scores in prediction.