LAUSR

In this article, a cost-sensitive graph convolutional networks (CSGCN) is proposed for crash severity analysis. To extract more complete data information, in addition to the original crash characteristics, CSGCN proposes to take the local neighborhood structure hidden among crashes as the input by defining a $k$-nearest neighbor graph, such that more robust model and significant crash contributing factors can be derived. Furthermore, to address the crash severity class imbalance issue, a cost-sensitive classification layer is developed in CSGCN, aiming to assign unequal cost values to crashes with different crash severity levels. Thereby, an unbiased classification hyperplane can be obtained, especially for estimating the relationship between crash influential factors and the minority but more severe crash severity level. In particular, in order to extract more discriminative crash feature representations, a cost-sensitive graph Laplacian regularisation layer is added to CSGCN. The crash dataset collected from Victorian from 2018 to 2019 is adopted for crash severity analysis. In comparison with 19 crash severity models, the proposed CSGCN can achieve at least 20.91% performance improvement in terms of the designed criterion of overall summary score. Moreover, the pseudo elasticity of the proposed CSGCN shows that the speed zone, accident time, heavy vehicles, road condition, run-off road and collision types are the most significant contributing factors to crash severity, providing implications to traffic engineers and policy makers for crash severity mitigation.