MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks
Thanh Minh Tran, Dan He, Jiwon Kim, Mark Hickman
Abstract
This study addresses the problem of predicting traffic incidents across a large-scale road network to support network-wide real-time traffic management. To enable network-wide incident prediction, traditional approaches either build a separate model for each target link (road segment) across the network or build one big model containing all the links in the network, which are costly and inefficient. Instead, we propose a sub-area level incident prediction model that can predict an incident occurrence within ‘any’ given sub-area across the network using a single model, where this single network-wide model learns incident occurrence patterns from a large number of randomly sampled sub-areas across the whole network. For this, we develop a Multi-structured Graph Neural Network (MSGNN) model that effectively captures spatio-temporal relationships among links within each sub-area, where multiple graphs with different structures are formed to represent different data sources for the same geographic sub-area and the combined graph embeddings from those multiple graphs are taken as input to predict a binary classification label for incident occurrence as output. We conduct a comprehensive set of experiments for model evaluation. When compared with benchmark models, which are chosen from classical machine learning models as well as recent deep learning models, our model outperforms the other models in most of the test cases, showing superior performance consistently across different parameter settings (15-min and 60-min prediction horizons) and different study networks (Brisbane and Gold Coast). We also demonstrate the benefit of the multi-structured graph input architecture in flexibly fusing heterogeneous data sources to enhance the model accuracy, in conjunction with a novel clustering-based data imputation method that allows us to fully leverage even a sparse dataset with many missing data.