## 6 Conclusions  
This study introduced the Deep Learning Classification of Traffic-Related Tweets (DLCTC) model, specifically designed to tackle the challenges of classifying short, informal texts from X data. By integrating character, word, and concept embeddings with advanced neural architectures like Bidirectional Long Short-Term Memory (BiLSTM) networks and attention mechanisms, the DLCTC model achieved a superior F1 score of 87.64% and an accuracy of 85.14% on the collected X dataset. This performance significantly surpasses traditional machine learning models such as Logistic Regression and Support Vector Machines, as well as standard deep learning models like LSTM and CNN, which struggled to capture the complex linguistic patterns and contextual nuances inherent in tweets.  
The success of the DLCTC model can be attributed to its multi-level embedding approach, which captures morphological nuances, semantic meanings, and contextual relationships within the tweets. The incorporation of the Probase knowledge base for concept embeddings was particularly effective in disambiguating words with multiple meanings based on context, enabling the model to differentiate between terms like \"crash\" as a traffic incident versus a computer malfunction. The meticulous preprocessing steps--including text normalization, handling of abbreviations and slang through Byte Pair Encoding, lemmatization, and conversion of emojis into textual descriptions--ensured that the data fed into the model was both clean and semantically rich.  
An ablation study underscored the importance of each component within the DLCTC model. The removal of character embeddings, concept embeddings, or the attention mechanism resulted in notable decreases in performance, highlighting their essential roles in the model's effectiveness. While the DLCTC model demonstrates significant improvements, certain limitations remain, such as handling extremely noisy data and computational complexity, which may hinder its deployment in real-time applications. Future work could focus on enhancing robustness to noisy data through more sophisticated normalization techniques, optimizing the model for speed, and extending support to multiple languages to increase its utility in global contexts.  
In conclusion, the DLCTC model represents a significant advancement in short-text classification within social media contexts. By effectively addressing the challenges posed by the informal and ambiguous language of tweets, it holds promise for applications not only in traffic monitoring but also in areas like sentiment analysis, emergency response, and public opinion tracking. The methodologies and findings from this study contribute to the broader field of natural language processing, particularly in handling noisy and short text data, and pave the way for future research and development in this domain.  
Conceptualization, W.Y.M.; Methodology, W.Y.M., A.A. and F.M.; Software, W.Y.M.; Validation, W.Y.M.; Formal analysis, W.Y.M.; Data curation, W.Y.M.; Writing--original draft, W.Y.M.; Writing--review & editing, A.A.; Visualization, W.Y.M.; Supervision, A.A. and F.M.; Project administration, A.A. and F.M.; Funding acquisition, A.A. and F.M. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the University of Derby.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors express their gratitude to the Data Science Research Centre (DSRC) at the University of Derby for their support in research the development. Additionally, the authors extend their thanks to the anonymous reviewers for their valuable contributions to this work.  
There are no conflicts of interest.