###### Abstract  
In this paper, a gated recurrent unit-deep neural network (GRU-DNN) model integrated with multimodal feature embedding (MFE) is developed to evaluate the real-time risk of hazmat road transportation based on various types of data for contributing factors. MFE was incorporated into the framework of a deep learning model in which discrete variables, continuous variables, and images were uniformly embedded. GRU is a pre-trained sub-model, and the DNN is able to directly use the relative structure and weights of the GRU, improving the poor classification and recognition results due to insufficient samples. Additionally, the model is trained and validated based on hazmat road transportation database consisting of 2100 samples with 20 real-time contributing factors and four risk levels in China. The accuracy (ACC), precision (PR), recall (RE), F1-score (F1), and areas under receiver-operating-characteristic curves (AUC) of the proposed model and other commonly used models are compared as performance measurements in numerical examples. Finally, Carlini & Wagner attack and three defenses of adversarial training, dimensionality reduction and prediction similarity are proposed in the training to improve the robustness of the model, alleviating the impact of noise and error on small-sized samples. The results demonstrate that the average ACC of the model reaches 93.51% and 87.6% on the training and validation sets, respectively. The prediction of accidents resulting in injury is the most accurate, followed by fatal accidents. Combined with the RE of 89.0%, the model exhibits excellent performance. In addition, the proposed model outperforms other widely used models based on the overall comparisons of ACC, AUC, F1 and PR-RE curve. Finally, prediction similarity can be used as an effective approach for robustness improvement, with the launched adversarial attacks being detected at a high success rate. risk assessment; hazardous materials transportation; deep neural network; multimodal feature embedding; adversarial attack +
Footnote †: journal: _Sciences_  
0000-0001-9993-3227][PERSON] 0000-0002-0002-3330-00  
such transportation is quite low (generally from \(10^{-8}\) to \(10^{-6}\)/km) [7; 8]. Road tankers with hazmat can be represented as potential \"mobile bombs\" that could result in serious casualties, property damage, and environmental pollution [9; 10; 11; 12; 13]. Real-time risk assessment of hazmat transportation is challenging due to the numerous risk factors, the unknown operational status of road tanks, and the variable nature of transportation environments [14]. Fortunately, GPS tracking and dashboard cameras have been compulsory in road tankers transporting hazmat in China in recent years. Real-time risk assessment for road transportation of hazmat becomes feasible when the operational condition of road tankers is monitored and various data are simultaneously transferred to RTAs. Risk assessment is one of the top priorities in the investigation of hazmat transportation, serving as the basis for risk mitigation [6; 15; 16; 17]. In traffic safety studies, the risk is represented by the occurrence of accidents, which is a binary variable (i.e., accident vs. non-accident) [18]. However, the accident rate for hazmat transportation is relatively low, with severity being the most striking feature for hazmat transportation accidents. Hence, the risk of road transportation of hazmat is evaluated on the basis of the occurrence and severity of accidents [19; 20]. The relationship between road transportation risk and contributing factors can be assessed on the basis of either parametric or nonparametric approaches. Parametric approaches include traditional statistical models [21; 22; 23; 24; 25; 26; 27], while nonparametric approaches are based on machine learning techniques [1; 3; 28]. Typical assumptions in traditional statistical models include deterministic data distribution and the existence of a linear relationship between the independent and dependent variables.