Geographical Information Alignment Boosts Traffic Analysis via Transpose Cross-attention

• URL: http://arxiv.org/abs/2412.02839v1
• Date: Tue, 03 Dec 2024 21:04:49 GMT
• Title: Geographical Information Alignment Boosts Traffic Analysis via Transpose Cross-attention
• Authors: Xiangyu Jiang, Xiwen Chen, Hao Wang, Abolfazl Razi,
• Abstract summary: We propose a plug-in-and-play module for common GNN frameworks, termed Geographic Information Alignment (GIA) This module can efficiently fuse the node feature and geographic position information through a novel Transpose Cross-attention mechanism. Our method can obtain gains ranging from 1.3% to 10.9% in F1 score and 0.3% to 4.8% in AUC.
• Score: 4.323740171581589
• License:
• Abstract: Traffic accident prediction is crucial for enhancing road safety and mitigating congestion, and recent Graph Neural Networks (GNNs) have shown promise in modeling the inherent graph-based traffic data. However, existing GNN- based approaches often overlook or do not explicitly exploit geographic position information, which often plays a critical role in understanding spatial dependencies. This is also aligned with our observation, where accident locations are often highly relevant. To address this issue, we propose a plug-in-and-play module for common GNN frameworks, termed Geographic Information Alignment (GIA). This module can efficiently fuse the node feature and geographic position information through a novel Transpose Cross-attention mechanism. Due to the large number of nodes for traffic data, the conventional cross-attention mechanism performing the node-wise alignment may be infeasible in computation-limited resources. Instead, we take the transpose operation for Query, Key, and Value in the Cross-attention mechanism, which substantially reduces the computation cost while maintaining sufficient information. Experimental results for both traffic occurrence prediction and severity prediction (severity levels based on the interval of recorded crash counts) on large-scale city-wise datasets confirm the effectiveness of our proposed method. For example, our method can obtain gains ranging from 1.3% to 10.9% in F1 score and 0.3% to 4.8% in AUC.

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