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

TL;DR

This paper introduces PASTA, a neural network that models complex spatio-temporal patterns in fine-grained city maps to accurately predict future crowd flows, outperforming existing methods especially in irregular regions.

Contribution

The paper proposes a novel neural network architecture with spatial auto-correlation gating, multi-scale residual blocks, and temporal attention gating for improved crowd flow prediction.

Findings

PASTA outperforms baseline models in accuracy.

Effective handling of irregular spatial regions.

High attention scores identify critical temporal features.

Abstract

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.