Cross-city crash severity analysis with cost-sensitive transfer learning algorithm

Abstract

Due to the difficulties of data collection in some cities and the relatively small portion of more severe crashes, this paper proposes a cost-sensitive transfer learning framework for more robust crash severity analysis. Specifically, to address the class imbalance issue, cost-sensitive learning method is adopted by assigning unequal cost values to different crash severity levels to obtain unbiased classification results. Moreover, due to the existence of city-irrelevant common crash contributing factors, cross-city crash severity analysis is developed, i.e., transferring common crash severity knowledge from other cities with transfer learning algorithm to assist crash severity modelling in target city that suffers from data scarcity problem. In this study, the crash datasets of Victorian Australia and Seattle are selected as the target and source domains respectively. To address the issue of heterogeneous explanatory features among these two datasets and extract the interpretable common crash severity knowledge, a feature alignment approach is proposed which can represent the cross-city data with unified feature representations on their original explanatory feature spaces. The two proposed models, i.e., cost-sensitive transfer logistic regression (CST-LR) and cost-sensitive transfer support vector machine (CST-SVM), have demonstrated better performance in comparison with twelve commonly used crash severity models, especially when target city crash data is scarce. The most significant crash contributing factors extracted by proposed model also show higher degree of consistency with the true contributing factors obtained from the target dataset, in comparison with the model built without transfer learning. The results could provide policy implications and counter measures for crash severity mitigation.