## 3 Results and Discussion
### Cocitation Analysis: Influential Journals, References and Authors  
Following standard VOSviewer suggested procedures, we performed a cocitation analysis to establish the more influential journals and references. Reference cocitation can allow observers to automatically recognize the shape, qualities and scientific progress of a research field. In the same way, journal cocitation analysis can be a useful instrument for assessing the general layout of a scientific field relative to the current theoretical framework of specific journals. The algorithm considers that two references or two journals are more strongly connected when they are more frequently cocited. Again, a clustering algorithm is applied to discover clusters of connected references and journals. In this procedure, we decided to include only references and journal sources with a minimum of three cocitations. The results show that, with 13,574 cited references, only 67 meet the threshold. For each of the sorted references, the total strength of the cocitation links with other cited references was calculated, and all 67 references are mapped in Figure 4. In an attempt to resolve the poor connection among these 67 references, the largest set of connected references was considered, which consists of 23 items. For each of the 23 sorted references, the total strength of the cocitation links with other cited references was calculated, and all 23 cocited references are listed and mapped in Figure 5. Five dispersed clusters surfaced, and in the visualization, it is still possible to see a poor connection among references. Clusters are differentiated by colors. Larger nodes indicate a higher number of citations, and the different clusters evidence the connection among references. The network of references is not well developed, and this seems to suggest that the specific field of research is in an embryonic stage with a great potential for growth: new findings are possible and new scientific works have the potential to create a more developed research network in the future. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ITS** & **Smart Cities and Traffic** & **Connected Vehicles and Traffic** \\ \hline intelligent transportation systems & internet of things & adaptive traffic signal control \\ \hline traffic lights & reinforcement learning & connected vehicles \\ \hline vehicular networks & smart cities & dynamic programming \\ \hline wireless sensor networks & traffic congestion & fuel consumption \\ \hline  & traffic flow & traffic signal optimization \\ \hline  & traffic light control & traffic signals \\ \hline  & traffic signal control & traffic simulation \\ \hline  & v2I & VANET \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Keyword topic visualization clusters. The largest node is represented by a work from [PERSON], [PERSON] and [PERSON] [57], which was not in the original database of 684 works. This procedure, in fact, is able to find influential documents that might not be present in the database list \"A\" and that might have received a high number of cocitations in the database. Among these 67 cocited documents, it is also possible to see general documents that constitute a scientific base for the research field under investigation but do not necessarily specifically pertain to this field of traffic signal control methods and experiments based on CAs and FCD. In order to consider documents that are influential for the investigated field of research, the 67 documents were manually checked to exclude completely impertinent documents; the following 15 documents listed in Table 2 have been considered as influential and will be added to the final list of influential documents (in case they are not already listed).