## 3 Materials and Methods
### DLCTC Model  
For the DLCTC model developed in this research, Algorithm 1 is used. The model integrates the following key embeddings: character embedding, word embedding, and concept embedding. ```
Input: Pre-trained PCA-transformed embeddings for character, word, and concepts with FastText: \(\mathbf{\chi_{train}}\), \(\mathbf{\chi_{test}}\), Number of training epochs E, Class weights \(\mathbf{w_{class}}\) Output: Performance metrics: Accuracy, Precision, Recall, F1 Score \(\mathbf{\chi_{train}}\leftarrow\mathbf{Load(train_{embeddings})}\) For (epoch = 1 to E)  For (# = 1 to \(\mathbf{\chi_{train}}\))  Hi\(\leftarrow\)BiLSTM(64)(pool)  Conv1D(256, 3) \(\rightarrow\) BatchNorm Conv1D(256, 4) \(\rightarrow\) BatchNorm Conv1D(256, 5) \(\rightarrow\) BatchNorm  Ai\(\leftarrow\)MultiHeadAttention(4 heads,64 key dimension)  End for output \(\leftarrow\) Dense(A, units=1, activation='sigmoid') Output: Trained model weights \(\mathbf{\theta}\). Performance loss \(\leftarrow\) BinaryCrossEntropy(output, labels) UpdateWeights(loss)  M \(\leftarrow\) CalculateMetrics(model, validation_data) End for return model.weights, M
```  
**Algorithm 1** Pseudo-code for the DLCTC model for short text classification  
The model operates in two phases: an offline phase for training the model with labeled social media data and an online phase where the trained model is applied to new messages from the dataset. In the offline phase, the model learns optimal representations and relationships within the training data. In the online phase, these learned representations allow the model to classify incoming messages accurately, providing insights for ITS. This model takes short social media messages as input, represented through multi-level embeddings, and the output of the model is a binary classification that labels each message as either traffic-related or non-traffic-related. The use of embedding techniques at different levels can effectively capture connections among linguistic units. At the character level, embedding represents characters as \begin{table}
\begin{tabular}{l l} \hline
**Tweet** & **Pre-Processed Text** \\ \hline Disabled Vehicle on Westbound highway WB & disable vehicle westbound highway wb Emily \\ at Emily Drive. Use caution when traveling & Emilye use caution travel area \\ \hline New Teacher Lunch \& training! Marker w/[PERSON]’s buzzwords! \#PowellNation & New tNewher lunch training marker \\ w/[PERSON]’s buzzwords! \#PowellNation & warGreta buzzword pollination our \\ \#ourcougarsourculture & cougarsourculture \\ \hline highway eastbound, all lanes open @ MM, NSP & Highway eastbound lane open nbsp run pace \\ running pace car operations starting near MM. & car operation start near mm \\ \hline \end{tabular}
\end{table} 
Table 2: Pre-processed text sample of the tweets. vectors in a high-dimensional space, enabling the model to capture their structural and phonetic features. At the word level, embedding represents words as dense vectors in a high-dimensional space, allowing the model to associate words that appear in similar contexts and comprehend a word's meaning based on its use in a sentence. At the sentence level, embedding represents sentences as vectors in a high-dimensional space, enabling the model to capture the overall meaning of a sentence by considering the relationships between individual words and their contexts [4]. The multi-level attention mechanism dynamically assigns weights to the most relevant tokens, emphasizing the critical parts of the text. Finally, the weighted sum of these token embeddings is aggregated to produce a comprehensive sentence-level embedding that represents the entire text, capturing both local token interactions and broader context, which is then used for classification. Consider the sentence \"Crash on I-95 near Exit 24\". The DLCTC model processes each word at multiple embedding levels. At the character level, words are broken down into individual characters, capturing morphological details--helpful for interpreting unknown or unusual words.