Cross-Task Crash Severity Analysis With Cost-Sensitive Transfer Graph Convolutional Network

Due to the difficulties in data collection or data barriers, some crash types may not have sufficient data for analysis, i.e., data scarcity in task (DST). Furthermore, as in crash severity analysis, the number of serious injury or fatality crashes is usually much smaller than that of property damage or injury crashes, there exists class imbalance issue, i.e., data scarcity in class (DSC). To tackle the DST problem, as common crash severity knowledge may hide among cross-task crashes, cross-task crash severity analysis is proposed. Intuitively, by transferring the common crash severity knowledge from source task which contains sufficient crashes to assist the modelling in target task that suffers from DST problem, more effective target task crash severity model can be obtained. Meanwhile, to address the DSC issue, the cost-sensitive learning algorithm for unbiased modelling is adopted by which unequal cost values are assigned to the crashes with different severity levels. In this paper, we develop a cost-sensitive transfer graph convolutional network, where the advanced graph convolutional network is taken as the base learner to guarantee the effectiveness of crash severity analysis. The vehicle-pedestrian and vehicle-vehicle crash severity analysis in city of Seattle are taken as the target and source tasks, respectively. In comparison with eighteen state-of-the-art crash severity models, the proposed model can achieve at least 21.29%, 8.10% and 8.02% improvement in terms of overall summary score, Macro-F1 and Macro-Gmean, respectively. The significant contributing factors analysis also shows that the additional crash contributing factors can be identified, including not only common factors among cross-task data, but also new ground-truth factors of target task crash severity analysis.