VANETs solutions use aggregated graph representation to model the interaction among the vehicles and different aggregated complex network measures to quantify some topological characteristics. This modeling ignores the temporal interactions between the cars, causing loss of information or unrealistic behavior. This work proposes the use of both temporal graphs and temporal measures to model VANETs applications. To verify the viability of this model, we initially perform a comparative analysis between the temporal and aggregated modeling considering five different real datasets. This analysis shows that the aggregated model is inefficient in modeling the temporal aspects of networks. After that, we perform a network evaluation through a simulation by considering the impact of temporal modeling applied to the deployment of RSUs. First, we compare a solution based on our temporal modeling with a greedy algorithm based on an aggregated model to choose the positions of RSUs. In a scenario with 70 RSUs, we have 77% and 65% of coverage in the temporal and aggregated model (greedy algorithm), respectively. Second, we evaluate the use of aggregated and temporal measures applied as features in a genetic algorithm. The approach with temporal betweenness had the better result with 90% of the coverage area against 61% of aggregated one applied to the same scenario.

VANETs 解决方案使用聚合图表示对车辆之间的交互进行建模，并使用不同的聚合复杂网络度量来量化一些拓扑特征。这种建模忽略了汽车之间的时间交互，导致信息丢失或不切实际的行为。这项工作建议使用时间图和时间度量来模拟 VANET 应用程序。为了验证该模型的可行性，我们首先在考虑五个不同的真实数据集的时间和聚合建模之间进行比较分析。该分析表明，聚合模型在对网络的时间方面建模时效率低下。之后，我们通过考虑应用于 RSU 部署的时间建模的影响，通过模拟进行网络评估。第一的，我们将基于时间建模的解决方案与基于聚合模型的贪婪算法进行比较，以选择 RSU 的位置。在具有 70 个 RSU 的场景中，我们分别在时间模型和聚合模型（贪婪算法）中拥有 77% 和 65% 的覆盖率。其次，我们评估在遗传算法中作为特征应用的聚合和时间度量的使用。具有时间介数的方法具有更好的结果，覆盖区域的 90% 与应用于相同场景的聚合区域的 61%。我们评估了在遗传算法中作为特征应用的聚合和时间度量的使用。具有时间介数的方法具有更好的结果，覆盖区域的 90% 与应用于相同场景的聚合区域的 61%。我们评估了在遗传算法中作为特征应用的聚合和时间度量的使用。具有时间介数的方法具有更好的结果，覆盖区域的 90% 与应用于相同场景的聚合区域的 61%。