Geographical Information Alignment Boosts Traffic Analysis via Transpose Cross-attention
This work improves traffic analysis for urban safety and congestion management by enhancing GNNs with geographic alignment, though it is incremental as it builds on existing GNN frameworks.
The paper tackles the problem of traffic accident prediction by addressing the underutilization of geographic position information in Graph Neural Networks, proposing a plug-in Geographic Information Alignment module with a Transpose Cross-attention mechanism that reduces computation costs and achieves gains of up to 10.9% in F1 score and 4.8% in AUC on city-wise datasets.
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.