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Understanding the movement patterns of objects (e.g., humans and vehicles) ina city is essential for many applications, including city planning andmanagement. This paper proposes a method for predicting future city-wide crowdflows by modeling the spatio-temporal patterns of historical crowd flows infine-grained city-wide maps. We introduce a novel neural network named PArallelSpatio-Temporal Attention with spatial auto-correlation gating (PASTA) thateffectively captures the irregular spatio-temporal patterns of fine-grainedmaps. The novel components in our approach include spatial auto-correlationgating, multi-scale residual block, and temporal attention gating module. Thespatial auto-correlation gating employs the concept of spatial statistics toidentify irregular spatial regions. The multi-scale residual block isresponsible for handling multiple range spatial dependencies in thefine-grained map, and the temporal attention gating filters out irrelevanttemporal information for the prediction. The experimental results demonstratethat our model outperforms other competing baselines, especially underchallenging conditions that contain irregular spatial regions. We also providea qualitative analysis to derive the critical time information where our modelassigns high attention scores in prediction.

PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction