M2NN: Multi-view multi-task graph neural network using congestion heatmap imagery for predicting traffic incidents across heterogeneous areas
Year: 2025
Type: article
Abstract: In Intelligent Transportation Systems (ITS), accurate and reliable traffic incident forecasting is essential for proactive traffic management. Traditional incident prediction methods face limitations: they either focus on specific road segments, missing the broader traffic network context, or conduct a global network-wide analysis, decreasing prediction specificity. These models also primarily rely on numeric data, such as traffic measures and network attributes. However, incorporating emerging data such as traffic map imagery can provide richer context to help models to better capture incident patterns and better generalise. This study addresses these challenges by introducing a Multi-view Multi-task Neural Network (M2NN) model, a deep graph neural network (GNN) that flexibly incorporates ‘multi-view’ inputs by combining traffic data with congestion heatmap images and adopts a ‘multi-task’ learning approach by leveraging both link-level incident prediction and subarea-level prediction as model’s target outputs. The evaluation results based on real-world data show that the M2NN model outperforms other benchmark models in link-level incident prediction tasks, which are highly challenging. The ablation analysis reveals that both the multi-view and multi-task learning components significantly enhance the prediction performance, demonstrating the benefit of the proposed architecture.
Authors: Thanh Tran, Dan He, Jiwon Kim, Mark Hickman
Institution: The University of Queensland
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,040
OpenAlex ID: W7106537463