PASTA：面向细粒度人流预测的带空间自相关门控的并行时空注意力机制
PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction
AI总结：
针对细粒度全市人流预测问题，提出含空间自相关门控、多尺度残差块、时间注意力门控的PASTA模型，能捕捉不规则时空模式，性能优于基线，还分析了高注意力关键时间信息。
摘要：
理解城市中对象（如行人和车辆）的移动模式对城市规划与管理等诸多应用至关重要。本文提出一种通过对细粒度全市地图中历史人流的时空模式建模，来预测未来全市人流的方法。我们提出了一种名为带空间自相关门控的并行时空注意力（PASTA）的新型神经网络，可有效捕捉细粒度地图的不规则时空模式。该方法的创新组件包括空间自相关门控、多尺度残差块和时间注意力门控模块。空间自相关门控利用空间统计学概念识别不规则空间区域；多尺度残差块负责处理细粒度地图中多范围的空间依赖关系；时间注意力门控则过滤掉与预测无关的时间信息。实验结果表明，我们的模型性能优于其他对比基线，尤其是在存在不规则空间区域的挑战性条件下表现更突出。我们还开展了定性分析，以推导模型在预测中赋予高注意力分数的关键时间信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
