Enterprise AI Analysis
Towards Resilient Transportation: A Conditional Transformer for Accident-Informed Traffic Forecasting
By Hongjun Wang et al. | December 10, 2025
Executive Impact: Enhancing Traffic Prediction for Resilient Transport
This paper introduces ConFormer, a novel conditional Transformer architecture for traffic prediction. By incorporating graph-based propagation adaptive normalization layers, ConFormer dynamically adjusts spatial and temporal correlations based on historical conditions. It demonstrates consistent superiority over mainstream spatio-temporal baselines and achieves superior predictive accuracy while preserving scalability for real-world deployment.
0%
MAE Improvement (PEMS03)
0%
MAE Improvement (Tokyo)
Lower
Computational Cost
Reduced
Parameter Demands
Deep Analysis & Enterprise Applications
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Motivation
Contribution
Methodology
Results
The Challenge of Unpredictable Traffic
Traffic prediction is challenged by external factors like accidents, causing sudden speed drops and complex changes that existing models struggle with. Real-world data shows accidents can increase travel times by 37-43%.
ConFormer: A Novel Conditional Transformer
The paper introduces ConFormer, a conditional Transformer with guided layer normalization. It also provides two new large-scale datasets for Tokyo and California highways, incorporating accident and regulation data.
Adaptive Spatiotemporal Modeling
ConFormer integrates graph propagation with guided normalization layers to dynamically adjust spatial and temporal node relationships based on historical patterns. This approach enhances predictive accuracy and efficiency.
State-of-the-Art Performance
ConFormer consistently outperforms state-of-the-art baselines like STAEFormer, achieving lower computational costs and reduced parameter demands across multiple metrics and datasets.
21.5%↑
Average MAPE Improvement on Tokyo Dataset
Enterprise Process Flow
Input Observations (X_t)
→
Spatiotemporal Embedding (X°)
→
Graph Propagation (X_c)
→
Conditional Factors (γ, β, α)
→
Transformer Branch (GLN, Attn, FF)
→
Future Traffic State (Ŷ_t)
| Feature | STAEFormer | ConFormer |
|---|---|---|
| Accident Awareness |
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| Computational Efficiency |
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| Accuracy in Accidents |
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| Dynamic Relationships |
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Accident Scenario Performance
ConFormer effectively captures evolving node distributions and sudden traffic velocity drops during accident scenarios, significantly outperforming competing models.
Key Takeaway: ConFormer's GLN mechanism induces significant feature shifts, enabling more accurate differentiation of abnormal traffic conditions, crucial for handling unpredictable events.
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