Instituto de Física, Universidad Nacional Autónoma de México, Ciudad Universitaria, Ciudad de México, México.
Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia.
PLoS One. 2024 Oct 29;19(10):e0312541. doi: 10.1371/journal.pone.0312541. eCollection 2024.
In this paper, we explore different methods to detect patterns in the activity of bus rapid transit (BRT) systems focusing on two aspects of transit: infrastructure and the movement of vehicles. To this end, we analyze records of velocity and position of each active vehicle in nine BRT systems located in the Americas. We detect collective patterns that characterize each BRT system obtained from the statistical analysis of velocities in the entire system (global scale) and at specific zones (local scale). We analyze the velocity records at the local scale applying the Kullback-Leibler divergence to compare the vehicular activity between zones. This information is organized in a similarity matrix that can be represented as a network of zones. The resulting structure for each system is analyzed using network science methods. In particular, by implementing community detection algorithms on networks, we obtain different groups of zones characterized by similarities in the movement of vehicles. Our findings show that the representation of the dataset with information of vehicles as a network is a useful tool to characterize at different scales the activity of BRT systems when geolocalized records of vehicular movement are available. This general approach can be implemented in the analysis of other public transportation systems.
在本文中，我们探讨了不同的方法来检测快速公交（BRT）系统活动中的模式，重点关注交通的两个方面：基础设施和车辆的运行。为此，我们分析了位于美洲的 9 个 BRT 系统中每个活跃车辆的速度和位置记录。我们从整个系统（全局尺度）和特定区域（局部尺度）的速度统计分析中，检测到了表征每个 BRT 系统的集体模式。我们在局部尺度上分析速度记录，应用相对熵（Kullback-Leibler divergence）来比较区域之间的车辆活动。这些信息组织在一个相似性矩阵中，可以表示为区域网络。然后，我们使用网络科学方法分析每个系统的结果结构。具体来说，通过在网络上实施社区检测算法，我们获得了不同的区域组，其特征是车辆运行的相似性。我们的研究结果表明，当有车辆运动的地理定位记录时，将数据集表示为网络的信息是一种有用的工具，可以在不同的尺度上描述 BRT 系统的活动。这种通用方法可以应用于其他公共交通系统的分析。
