In this study, a fusion convolutional neural network with random term (FCNN-R) model is proposed for driver injury severity analysis. The proposed model consists of a set of sub-neural networks (sub-NNs) and a multi-layer convolutional neural network (CNN). More specifically, the sub-NN structure is designed to deal with categorical variables in crash records; multi-layer CNN structure captures the potential nonlinear relationship between impact factors and driver injury severity outcomes. Seven-year (2010–2016) single-vehicle crash data is applied. Models with different CNN layers are tested using the validation set, as well as various model layouts with and without a dropout layer or regularization term in the objective function. It is found that different model layouts provide consistent predictive performance. With the limited training data, more CNN layers result in the prematurity of the training procedure. The dropout layer and the regularization technique help improve the stability of the effects of different variables. The proposed model outperformed other five typical approaches in the predictability comparison. Moreover, a marginal effect analysis was conducted to the proposed FCNN-R model, the FCNN model and the mixed multinomial logit model. It shows that the proposed FCNN-R model can be used to uncover the underlying correlations similar to the traditional statistical models. Additionally, the temporal stability of the proposed FCNN-R approach is discussed based on the model performance in different years. Future research is recommended to include more information for improving the universality of the proposed approach.

在这项研究中，提出了一种带有随机项的融合卷积神经网络（FCNN-R）模型，用于驾驶员伤害严重性分析。所提出的模型由一组亚神经网络（sub-NNs）和一个多层卷积神经网络（CNN）组成。更具体地说，sub-NN结构旨在处理崩溃记录中的类别变量。多层CNN结构捕获了影响因素与驾驶员伤害严重程度结果之间的潜在非线性关系。应用了七年（2010-2016年）的单车碰撞数据。使用验证集以及目标函数中带有或不带有退出层或正则项的各种模型布局，对具有不同CNN层的模型进行测试。发现不同的模型布局提供了一致的预测性能。由于培训数据有限，更多的CNN层会导致训练过程过早。辍学层和正则化技术有助于提高不同变量的效果的稳定性。在可预测性比较中，建议的模型优于其他五种典型方法。此外，对所提出的FCNN-R模型，FCNN模型和混合多项式logit模型进行了边际效应分析。它表明，与传统的统计模型类似，所提出的FCNN-R模型可用于揭示潜在的相关性。此外，基于不同年份的模型性能，讨论了所提出的FCNN-R方法的时间稳定性。建议未来的研究包括更多信息，以改善建议方法的通用性。