Comprehensive—Model Based on Time Series for the Generation of Traffic Knowledge for Bus Transit Rapid Line 6 of México City

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.