# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 3 Experiments
### Simulation Experiments  
In total, 2378 road segments were selected from the real road network in Beijing to construct the simulated dataset. We generated simulated vehicle movement datasets at three snapshots (Figure 3). At each snapshot, the communities were generated using the procedure developed by [PERSON] et al. [15]. At the first snapshot, we randomly combined spatially adjacent road segments to construct the ranges of \(m\) spatial communities (the size of each community \(n_{i}\in[50,200]\)). Then, for each spatial community with \(n_{i}\) road segments, we randomly generated \(10n_{i}\) OD pairs in that community. Finally, we randomly set \(\sum 2n_{i}\) OD pairs as noise in the study area. The seven kinds of community evolution patterns  
Figure 2: Seven kinds of community evolution processes.  
were predefined in the simulated datasets. From Figure 3, one can see that there were communities with different shapes and sizes in the simulated datasets.  
The NMI index was used to quantitatively evaluate how well the communities identified by the two methods matched the predefined communities. The NMI value can be between 0 and 1 (with 1 representing a perfect match). The balance coefficient \(\gamma\) was set from 0.1 to 0.9, with a step size of 0.1. The NMI values of the experimental results obtained by the two methods are listed in Table 3. One can see that EACO performs better than FacetNet. EACO performs best when \(\gamma\) = 0.8 (Figure 4). FacetNet performs best when \(\gamma\) = 0.9 (Figure 5). DYNAMOGA can automatically determine the balance coefficient. The experimental results of DYNAMOGA are displayed in Figure 6. The NMI values of the communities identified at the three snapshots were 0.51, 0.42, and 0.43, respectively. Figure 7 displays the communities identified by RVLA. The NMI values of the communities identified at the three snapshots were 0.64, 0.52, and 0.53, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Snapshot** & \(\gamma\) = 0.1 & \(\gamma\) = 0.2 & \(\gamma\) = 0.3 & \(\gamma\) = 0.4 & \(\gamma\) = 0.5 & \(\gamma\) = 0.6 & \(\gamma\) = 0.7 & \(\gamma\) = 0.8 & \(\gamma\) = 0.9 \\ \hline \multirow{4}{*}{FacetNet [19]} & 1 & 0.52 & 0.58 & 0.60 & 0.63 & 0.65 & 0.66 & 0.69 & 0.71 & 0.73 \\  & 2 & 0.50 & 0.55 & 0.57 & 0.61 & 0.63 & 0.64 & 0.67 & 0.69 & 0.71 \\  & 3 & 0.49 & 0.51 & 0.53 & 0.57 & 0.61 & 0.63 & 0.64 & 0.68 & 0.70 Figure 4: Communities identified by EACO: (**a**) snapshot 1; (**b**) snapshot 2; (**c**) snapshot 3.  
Figure 5: Communities identified by FacetNet: (**a**) snapshot 1; (**b**) snapshot 2; (**c**) snapshot 3.  
From Figures 5-7, one can see that some predefined communities cannot be accurately identified by the three composition methods. Most of the predefined communities were wrongly segmented. DYNMOGA and RWLA identified some spatially dispersed communities, which were spurious communities formed by noise. In contrast, the predefined communities at different snapshots were well discovered by EACO. In Figure 8, the evolution of the communities identified by EACO is displayed. All seven kinds of community evolution patterns were well revealed by EACO.  
Figure 6: Communities identified by DYNMOGA: (**a**) snapshot 1; (**b**) snapshot 2; (**c**) snapshot 3.  
Figure 7: Communities identified by RWLA: (**a**) snapshot 1; (**b**) snapshot 2; (**c**) snapshot 3.