## 3 Dataset Analysis
### Analysis of Literature
#### 3.4.3 Words Analysis
Similar with bigrams, most of the trigrams are related to the agent-based modelling domain or transportation domain. In abstracts, the top 10 trigrams are \"agentbased model abm\", which has 65 occurrences, \"agent based model\" with 39 appearances, \"agent_based modelling abm\" with 32 appearances, \"agent_based modeling abm\", which has 31 occurrences, \"agent_based simulation model\" with 31 occurrences, \"electric vehicles evs\", which has 24 occurrences, \"agent_based models abms\" with 21 appearances, \"agent based modeling\" with 18 occurrences, and \"agent_based modeling approach\" and \"agent_based modeling framework\" with 16 appearances for each one. In titles, the top 10 most popular trigrams are \"agent_based modeling approach\", which has 19 appearances, \"electric vehicle charging\" with 15 occurrences, \"agent_based modelling approach\", which has 11 occurrences, \"agent based model\" and \"agent_based modelling framework\" with 8 occurrences for each one, \"agent_based simulation approach\" with 7 appearances, and \"agent based modelling\", \"agent_based simulation model\", \"electric vehicle adoption\", and \"vehicle charging infrastructure\" with 6 appearances for each one. Furthermore, as most of the identified n-grams are related to the ABM field, we have further proceeded to eliminating some of the ABM-specific bigrams from abstracts, as well as eliminating bigrams related to the model or some other methods used in the paper. Some examples of the removed bigrams are \"agent_based model\", \"agent_based modelling\", \"agent_based models\", \"agent_based modelling\", \"simulation results\", \"model abm\", \"agent based\", \"simulation model\", \"agent_based simulation\", \"proposed model\", \"based model\", \"modeling framework\", \"mode choice\", \"modeling abm\", \"modelling abm\", \"empirical data\", \"simulation models\", \"modeling approach\", \"system dynamics\", \"experimental results\", \"model results\", \"modelling approach\", and \"models abms\". As a consequence of this curation, the bigrams related to the transportation field have been identified as presented in Table 12.  
From Table 12, it can be observed that a series of bigrams are related to road transport (e.g., \"electric vehicles\", \"road network\", \"traffic congestion\", \"traffic flow\", \"electric vehicle\", \"charging stations\"), while others refer to air transport (e.g., \"boarding methods\"). Nevertheless, a series of bigrams are related to public transport in general, such as \"public transport\", \"travel demand\", \"transportation system\", \"travel time\", and \"transportation systems\". Few bigrams discuss the effects of transportation over climate (e.g., \"climate change\", \"air pollution\") or on public health (e.g., \"social distancing\"). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Trigrams in Abstracts** & **Occurrences** & **Trigrams in Titles** & **Occurrences** \\ \hline agent\_based model abm & 65 & agent\_based modeling approach & 19 \\ agent\_based model & 39 & electric vehicle charging & 15 \\ agent\_based modelling abm & 32 & agent\_based modelling approach & 11 \\ agent\_based modeling abm & 31 & agent based model & 8 \\ agent\_based simulation model & 31 & agent\_based modeling framework & 8 \\ electric vehicles evs & 24 & agent\_based simulation approach & 7 \\ agent\_based models abms & 21 & agent based modelling & 6 \\ agent based modeling & 18 & agent\_based simulation model & 6 \\ agent\_based modeling approach & 16 & electric vehicle adoption & 6 \\ agent\_based modeling framework & 16 & vehicle charging infrastructure & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Top 10 most frequent trigrams in abstracts and titles. Given the evolution of the annual scientific production provided in Figure 2, three distinct temporal periods emerge: 2002-2012, 2013-2017, and 2018-2023. These distinct periods are characterized by significant deviations in annual publication trends.