Cost-Sensitive Graph Convolutional Network With Self-Paced Learning for Hit-and-Run Analysis

This article aims to tackle two issues in hit-and-run crash analysis: The first one is the missing label issue, as the hit-and-run label information has been ignored by many crash datasets. Another one is the class imbalance problem, as the number of hit-and-run crashes is usually much smaller than that of non-hit-and-run crashes. The semi-supervised learning by manually labelling a few hit-and-run labels and the cost-sensitive learning that assigns unequal cost values to class-imbalanced crashes are adopted in this paper. Specifically, we develop a cost-sensitive graph convolutional network with self-paced learning (CSGCNsp) for hit-and-run analysis, which takes the proposed
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-nearest neighbour anchor graph as input to extract the local clustering structure hidden among crashes for more effective convolutions. The proposed method performs semi-supervised learning with an ‘easy-to-hard’ manner, which can help infer more accurate hit-and-run labels for unsupervised crashes with imbalanced data distribution. The road traffic collision database of Victoria State of Australia is selected for hit-and-run analysis. Compared to fifteen other models, the proposed CSGCNsp can significantly improve the prediction performance in terms of AUC, G-mean, F-measure, and total misclassification cost. The significant contributing factors analysis shows that 53.33% of true contributing factors, which is at least 20.00% higher than that of the state-of-the-art hit-and-run models, can be found by CSGCNsp when only 1% of hit-and-run crashes are manually labelled.