## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.3 Words Analysis
\begin{table}
\begin{tabular}{l r} \hline \hline  & **Words** & **Occurrences** \\ \hline agent-based model & 214 \\ agent-based modeling & 208 \\ agent-based modelling & 119 \\ simulation & 59 \\ electric vehicles & 46 \\ agent-based simulation & 41 \\ agent-based models & 28 \\ multi-agent systems & 18 \\ COVID-19 & 17 \\ airplane boarding & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Top 10 most frequent words in authors’ keywords. Cluster B, on the other hand, concentrates on authors' keywords. This Word Cloud features the most frequently occurring terms within this category, such as \"agent-based modelling\", \"simulation\", \"electric vehicles\", \"COVID-19\", \"agent-based models\", \"air-plane boarding\", \"optimization\", \"traffic simulation\", \"agent-based simulation\", \"climate change\", and \"agent-based modeling\". Considering the two Word Clouds presented in Figure 16, it can be observed that the authors' keywords are focusing more on the modeling and analysis techniques used in their papers (e.g., \"agent-based modelling\", \"agent-based models\", \"agent-based modeling\", \"agent-based simulation\") and the field of the transportation area in which they have been used (e.g., \"electric vehicles\", \"airplane boarding\", \"traffic simulation\"), while with the keywords plus, which are automatically generated by the WoS platform, the focus is more on generic words related to the analysis conducted in the paper, rather than on the various fields in the transportation field. Regarding the length of the keywords, it can be observed that the authors' keywords are composed from several words (in general bigrams), while the keywords plus are composed of a single word (unigrams). Table 10 presents a valuable analysis of the ten most frequently occurring word pairs, or bigrams, within the abstracts and titles of the analyzed papers. This analysis sheds light on the predominant word combinations that are commonly used in the corpus of research. In the abstracts, the most prevalent bigram is \"agent-based model\" with a substantial 565 occurrences. It is followed by \"agent-based modeling\" with 224 appearances, \"electric vehicles\" with 137 instances, \"agent-based models\" with 136 mentions, \"agent based modeling\" with 121 instances, \"simulation results\" with 90 occurrences, \"agent based\" with 87 appearances, and \"simulation model\" with 79 mentions, while \"model abm\" and \"agent_based simulation\" complete the list with 78 and 74 occurrences, respectively. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Bigrams in Abstracts** & **Occurrences** & **Bigrams in Titles** & **Occurrences** \\ \hline agent\_based model & 565 & agent\_based model & 153 \\ agent\_based modeling & 224 & agent\_based modeling & 105 \\ electric vehicles & 137 & agent\_based modelling & 67 \\ agent\_based models & 136 & agent\_based simulation & 30 \\ agent\_based modelling & 121 & electric vehicle & 43 \\ simulation results & 90 & electric vehicles & 31 \\ agent based & 87 & agent based & 28 \\ simulation model & 79 & agent\_based approach & 24 \\ model abm & 78 & modeling approach & 23 \\ agent\_based simulation & 74 & agent\_based models & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top 10 most frequent bigrams in abstracts and titles. Figure 16: Top 50 words based on keywords plus (**A**) and authors’ keywords (**B**). Similarly, in the titles of the papers, notable bigrams include \"agent-based model\" with 153 occurrences, \"agent based modeling\" with 105 mentions, \"agent-based modelling\" with 67 appearances, and \"agent-based simulation\" with 30 occurrences. Other significant bigrams include \"electric vehicle\" and \"electric vehicles\" (43 and 31 occurrences each, respectively), \"agent based\" (28 occurrences), \"agent-based approach\" (24 occurrences), and \"modeling approach\" and \"agent-based models\" (23 and 22 occurrences each, respectively). Table 11 presents top 10 most frequent trigrams found in abstracts and titles.