Figure 6

Community detection and activity of vehicles for similarity networks. (a) Similarity network with threshold value \(H=0.357\) with two communities \({\mathscr {C}}_1\) and \({\mathscr {C}}_2\). (b) Segments map representing the communities. Probability densities \(\rho (v)\) for the segments in \({\mathscr {C}}_1\) (c) and \({\mathscr {C}}_2\) (d). In panels (c) and (d), thin lines represent the \(\rho ^{(i)}_{\mathrm {total}}(v)\) for each segment as in Fig. 2b and thick dashed lines depict the probability density found for all the values of v in \(i\in {\mathscr {C}}_1\) and \(i\in {\mathscr {C}}_2\). The network in panel (a) was generated using Mathematica 12.3.1 (https://www.wolfram.com/mathematica/). (b)–(d) were created using python 3.8 and the matplotlib (3.4.3) package (https://matplotlib.org). The map in panel (b) was created using the geopandas (0.10.2) package (https://geopandas.org).