## 3 Literature Review
### Traffic
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Focus** \\ \hline [PERSON] et al. [71] & Pre-trained LLMs for traffic signal control by learning a more realistic policy \\ [PERSON] et al. [72] & Classifying pedestrian maneuver types from unstructured textual content \\ [PERSON] et al. [73] & Applying GPT-4V for risk perception in forward-facing traffic images \\ [PERSON] et al. [74] & Improving the automatic speech recognition in air traffic control \\ [PERSON] et al. [75] & Long-range traffic flow forecasting with a pre-trained model \\ [PERSON] et al. [76] & Developing human-like driving styles with a ‘coach agent’ module based on LLMs \\ [PERSON] et al. [77] & Analyzing air traffic management documents for optimizing operations \\ [PERSON] et al. [78] & Travel behavior prediction without data-based parameter learning \\ [PERSON] et al. [79] & Video Question Answering (VidQA) in intelligent traffic monitoring \\ [PERSON] [80] & Personalized driver assistance in electric vehicles (EVs) \\ [PERSON] et al. [81] & Reboost LLM-based Text-to-SQL for identifying traffic flow patterns \\ [PERSON] et al. [82] & LLMs with a transformer-based decoder for dynamic traffic scene generation \\ [PERSON] et al. [83] & Developing ChatGPT to solve complex mixed traffic control problems \\ [PERSON] et al. [84] & Analyzing the information extraction performance of LLMs in air-traffic control \\ [PERSON] et al. [85] & Serving as co-pilots for path tracking control and trajectory planning \\ [PERSON] et al. [86] & Performing situational decision-making for traffic monitoring \\ [PERSON] et al. [87] & Different applications of ChatGPT in the domain of intelligent vehicles \\ [PERSON] et al. [88] & Aiding human decision-making in traffic control \\ [PERSON] et al. [89] & Analyzing the spatio-temporal dynamics of traffic across road networks \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Studies about LLM applications in traffic. pedestrians. Compared with multinomial logit, random forest, and neural networks, LLMs performed great reasoning abilities to predict travel behavior [78]. Ref. [82] first established the urban context driver agent simulation framework based on LLMs, adapted to the rules conveniently by using natural language and cases. With the integration of LLM and a transformer-based decoder architecture, ref. [76] identified map locations, traffic distribution, and dynamic features of vehicles in the simulation framework. Moreover, it further showed that this type of model has great potential in driving strategy evaluation and traffic scenario settings. When LLMs are introduced into traffic monitoring systems, various schemes are necessary to build a safe and efficient intelligent transportation system, including the identification of vehicles in traffic, traffic flow patterns, risk perception, and many more. Ref. [73] investigated the potential of ChatGPT in the domain of traffic monitoring, which could detect risk estimates for driving scenarios. As traffic-domain knowledge is combined with video-language models, ref. [79] developed a novel method for traffic-focused reasoning tasks, namely traffic-domain video question answering with automatic captioning. Ref. [81] explored the Text-to-SQL, Text-to-Python as well as Text-to-Function options with LLMs in traffic congestion, providing an inspiring vision for this application. Ref. [86] devised three novel text-based models for different reasoning tasks: BDD-QA based on the Berkeley deep-drive explanation dataset for situational decision-making, TV-QA based on the Traffic-QA dataset for event factor analysis, and HDT-QA based on the human driving test question answering dataset for solving human driving exams. Ref. [89] captured the dynamic information of vehicles in the network systems from spatial and temporal levels, addressing uncertainties in the underlying data, through graph neural networks (GNNs) and transformer architectures.