## 5 Conclusions  
To assess the real-time risk of hazmat road transportation based on various types of data for contributing factors, this paper developed a gated recurrent unit-deep neural network (GRU-DNN) model integrated with multimodal feature embedding (MFE). MFE was incorporated into the framework of the deep learning model, in which discrete variables, continuous variables, and images were uniformly embedded. GRU was a pre-trained sub-model, the relative structure and weight of which could subsequently be used directly by the DNN, improving the poor classification and recognition results due to the insufficient number of samples. Then, the model was trained and validated based on a hazmat road transportation database of 2100 samples with 20 real-time contributing factors and four risk levels. Furthermore, the performance measures were evaluated in the numerical examples, whereby the accuracies (ACCs), precisions (PRs), recalls (REs), F1-scores (F1s) and areas under receiver-operating-characteristic curves (AUCs) were compared between the proposed model and other widely used models. Finally, Carlini & Wagner attack and three corresponding defenses--adversarial training, dimensionality reduction and prediction similarity--were proposed in the training to improve the robustness of the model, alleviating the impact of noise and error on the small-sized samples.  
The results demonstrated that the average ACC of the model was able to reach 93.51% and 87.6% on the training and validation sets, respectively. The prediction of injury accidents was the most accurate, followed by fatal accidents. Combined with the 89.0% RE, the model demonstrated excellent performance at real-time risk assessment of hazmat road transportation. In addition, the proposed model outperformed DNN, Convolutional Neural Network, Mixed Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest based on the overall comparisons of the ACC, AUC, F1 and PR-RE curve. Finally, prediction similarity was an effective approach for improving the robustness of GRU-DNN with MFE, with the launched adversarial attacks being detected with a high success rate.  
In future research, more variables will be considered. Meanwhile, multimodal data fusion will adopt more fusion methods to better analyze the state of road transportation of hazardous materials. On the other hand, data on the road transportation of hazardous materials is insufficient. Therefore, more robust prediction models are needed for small numbers of samples. At the same time, during the model deployment process, the mechanism of continuously updating the model based on the difference between the predicted data and the real data should also be explored in the future.  
Conceptualization, S.Y. and Y.L. ([PERSON]); methodology, S.Y., Y.L. ([PERSON]) and Y.L. ([PERSON]); software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]); investigation, S.Y. and G.L.; resources, G.L.; data curation, Y.L. ([PERSON]); writing--original draft preparation, S.Y., Y.L. ([PERSON]) and Z.X.; writing--review and editing, S.Y. and Y.L. ([PERSON]); visualization, S.Y.; supervision, Y.L. ([PERSON]); project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 71901190 and Project for Science and Technology Plan of Guangxi, China, grant number AB20159032.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.