## 2 Model
### Multimodal Feature Embedding  
As mentioned above, the forecast data in this paper involve multiple categories, and cannot be used for subsequent analysis with the same model. Therefore, the first step of the proposed framework is to use the multimodal representation to map the source data of multiple modalities to the same feature representation space. Information is represented as numerical vectors that can be analyzed by computers or further abstracted into higher-level feature vectors thanks to unimodal representation learning. Multimodal representation learning refers to learning better feature representations by taking advantage of the complementarity between multimodalities and eliminating the redundancy between \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attributes for** & **Contributing Factor for** & **Type of** & & **Value of Data** & **Source of** \\
**Accident Occurrence** & **Attributes** & **Data** & & **Data** \\ \hline \multirow{6}{*}{severity} & & & 0 = explosive & \\  & & & 1 = flammable & \\  & physicochemical property & & 2 = corrosive & \\  & of hazard \(S_{2}\) & & 3 = oxidative & \\  & & & 4 = poisonous & \\  & & & 5 = radiative & \\ \cline{2-5}  & ratio of hazard amount to & & & \\  & tank volume \(S_{3}\) & C & [0, 92] (\%) & II \\ \cline{2-5}  & & & 0 = no leakage & \\  & & & 1 = permeating leakage & \\  & leakage of hazard \(S_{4}\) & D & 2 = water-clock leakage & I and VI \\  & & & 3 = heavy leakage & \\  & & & 4 = flowing leakage & \\ \hline \multirow{6}{*}{social influence} & & & 0 = no & \\  & sensitive period \(I_{1}\) & D & 1 = holiday & \\  & & & 2 = festival & \\  & & & 3 = other large-scale activities & \\ \cline{2-5}  & traffic condition \(I_{2}\) & D & 0 = uncongested & \\  & & & 1 = congested & VI \\  & & & 2 = heavily congested & \\ \cline{2-5}  & & & 0 = no vulnerable community & \\  & vulnerable community & D & 1 = school & IV and VII \\  & passed by \(I_{3}\) & D & 2 = hospital & \\  & & & 3 = large community & \\ \cline{2-5}  & & & 0 = no vulnerable natural region & \\  & vulnerable natural region & D & 1 = river & \\  & passed by \(I_{4}\) & D & 2 = reservoir or lake & IV and VII \\  & & & 3 = forest & \\ \hline \hline \end{tabular} Note: Type of data: C: continuous variable; D: discrete variable; E: image. Source of data: I: real-time sensors in vehicle; II: data transferred to RTA before transporting; III: database in RTA; IV: real-time GPS tracking; V: real-time dashboard camera; VI: real-time data in TMC; VII: data in GIS in RTA.  
\end{table} 
Table 2: _Cont._modalities. Through multimodal representation learning, the data of different modalities can be complemented, ambiguity and uncertainty can be eliminated, and more accurate judgment results can be obtained. The overall pipeline is shown in Figure 2.  
Before feature fusion, a Variational Autoencoder (VAE) [63] is employed to reconstruct data from different modalities into high-dimensional features of a specific distribution. As a generative model, VAE first transforms the real samples into a specific data distribution through an encoder network. This data distribution is then passed to a decoder network to obtain the corresponding generated samples. The autoencoder model [64] is trained to ensure the generated samples are close enough to the real samples. It can be seen that autoencoder models do not need to use the label of the sample in the optimization process. This unsupervised optimization method greatly improves the versatility of the model. As shown in Figure 3, the encoder modally transforms the discrete vector \(x_{d}\) and images \(x_{i}\) into latent features:  
\[ \begin{split} h_{d}&=g(W_{d}x_{d}+b_{d})\\ h_{i}&=g(W_{i}x_{i}+b_{i})\end{split}  \tag{1}\]  
Figure 1: Recognitions for risky driving and weather conditions from dashboard camera. Figure 2: Multimodal feature embedding. Then, the sampled latent vectors are concatenated and mapped to a specific distribution. Finally, the latent vector in a common space was reconstructed into high-dimensional features for subsequent data fusion.