Mobile sensor networks consist of different types of integrated devices that collect, disseminate, process and store information from the environments in which they are implemented. This type of network allows for the development of applications and systems in different areas for the generation of knowledge. In this paper, we propose a model called the Metrobus Arrival Prediction (MAP) model for predicting the arrival times of Line 6 buses of the bus rapid transit (BTR) system, known as the Metrobus, in Mexico City (CDMX). The network is composed of mobile and static nodes that collect data related to the speed and position of each Metrobus bus. These data are sent to the proposed time series model, which yields the Metrobus arrival time estimation. MAP allows the density of users projected during the day to be estimated with a time series model that uses the data collected and the historical data of each station. A comparison is made between the model results and the arrival time obtained with real-time traffic monitoring applications, such as Moovit and Google Maps. The proposed model, based on time series, takes the historical data (data of trajectory times) as reference to start the first arrival times. From these values, MAP feeds on the data collected through the sensor network. As the data are collected through the sensor network, the estimates present results, for example, the mean absolute error (MAE) of the expected time was less than 0.2 s and the root mean square error (RMSE) of the expected value was below 1 for the proposed model. Compared to real-time traffic platforms, it presents a value of 0.1650 of the average dispersion obtained in travel times. The obtained values provide certainty that the data shown presents results as accurately as a real-time platform that requires the data at the moments in which the traffic variations occur. Moreover, unlike other state-of-the-art models that rarely interact on the site, MAP requires a reduced number of variables, being an accessible tool for the implementation and scaling of real-time traffic monitoring.

中文翻译：

---

综合——基于时间序列的墨西哥城公交快速6号线交通知识生成模型

移动传感器网络由不同类型的集成设备组成，这些设备收集、传播、处理和存储来自其实施环境的信息。这种类型的网络允许在不同领域开发应用程序和系统以产生知识。在本文中，我们提出了一种称为 Metrobus Arrival Prediction (MAP) 模型的模型，用于预测墨西哥城 (CDMX) 的快速公交 (BTR) 系统 (称为 Metrobus) 的 6 号线公交车的到达时间。该网络由移动和静态节点组成，它们收集与每条 Metrobus 总线的速度和位置相关的数据。这些数据被发送到建议的时间序列模型，从而产生 Metrobus 到达时间估计。MAP 允许使用时间序列模型估计白天预计的用户密度，该模型使用收集的数据和每个站点的历史数据。将模型结果与使用实时交通监控应用程序（如 Moovit 和谷歌地图）获得的到达时间进行比较。所提出的模型，基于时间序列，以历史数据（轨迹时间数据）为参考，开始首次到达时间。根据这些值，MAP 以通过传感器网络收集的数据为基础。由于数据是通过传感器网络收集的，因此估计呈现结果，例如，预期时间的平均绝对误差（MAE）小于0.2 s，预期值的均方根误差（RMSE）低于1对于建议的模型。与实时流量平台相比，它表示在旅行时间中获得的平均分散值为 0.1650。获得的值提供了确定性，即显示的数据与实时平台一样准确地呈现结果，该平台需要在交通变化发生的时刻提供数据。此外，与很少在站点上交互的其他最先进的模型不同，MAP 需要减少的变量数量，是实施和扩展实时流量监控的可访问工具。