## 3 Literature Review
### Other  
In addition to LLM applications in the above-mentioned areas, researchers and practitioners also have focused on the development and application of LLMs in the context of other transportation-related problems. In particular, building domain-specific models seems to be an emerging topic in the literature. Moreover, exploring the potential of LLMs in the logistics industry could play a critical role; see Table 5 for the characteristics of extant studies related to other LLM applications for transportation. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Focus** \\ \hline [PERSON] et al. [90] & A knowledge base integration method for question-answering in the aviation domain \\ [PERSON] [91] & Enhancing the ability of ChatGPT in aerospace engineering \\ [PERSON] and [PERSON] [92] & Analyzing ChatGPT’s answers for transport issues and solutions \\ [PERSON] et al. [93] & Developing a transit-topic-related LLM to classify open-ended text \\ [PERSON] et al. [94] & Providing challenges of AI-based applications in aviation \\ [PERSON] et al. [95] & Classifying the design, functional, and performance requirements in aviation \\ [PERSON] et al. [96] & Developing an annotated aerospace corpus and fine-tuned BERT language model \\ [PERSON] [97] & Exploring the use of ChatGPT in public transportation \\ [PERSON] et al. [98] & A two-stage adapting sentence transformer-based model in the aviation domain \\ [PERSON] et al. [99] & Achieving the versatility to tackle NLP problems in the aviation domain \\ [PERSON] and [PERSON] [100] & A fine-tuned LLM to polarize customer sentiments automatically \\ [PERSON] et al. [101] & Language model to predict customer calling of logistics \\ [PERSON] and [PERSON] [102] & GPT-3 Codex to develop simulation models for logistics \\ [PERSON] et al. [103] & Human-AI collaboration to model logistics systems \\ [PERSON] [104] & Impacts of ChatGPT on third-party logistics operators \\ [PERSON] [105] & Applications of ChatGPT within the logistics domain \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Studies on LLM applications in other transportation domains. As LLMs have demonstrated excellent performance in a variety of natural language tasks, efficient transportation-specific understanding could have a positive effect on knowledge integration [99]. In the aviation domain, ref. [90; 98] built question-answering (QA) systems with an aviation corpus, highlighting the significance of LLMs. Ref. [95] employed a BERT-based model (i.e., aeroBERT-Classifier) to identify different types of aerospace requirements, ref. [96] created a related aviation corpus where a set of aerospace requirements were included with technical terms and language. When it comes to aerospace engineering problems, ref. [91] took the [PERSON] method as an example to uncover the utilization of ChatGPT, which indicated the excellent mathematical ability of ChatGPT. Ref. [97] considered the topic related to public transportation and utilized ChatGPT to provide meaningful ideas and thoughts. LLMs can also be used to provide an introduction to transport problems and corresponding solutions and to design and assess flight crew staff training [92; 94]. Furthermore, satisfying the customers' demands and requirements is known to be significantly more beneficial to solving transportation-specific problems. For the airline and aviation industry, ref. [100] implemented a customer relationship management (CRM) system according to a custom dataset from five Indian airlines. Ref. [93] analyzed the transit riders' feedback in ridership surveys by a transit-topic-aware LLM method. In the logistics industry, LLMs can be used for various operations, such as analyzing sophisticated numerical data, providing actionable insights, managing assortment, forecasting demand, and vehicle routing problems [104; 105]. To predict customer calls asking shipment status, ref.