Activity of vehicles in the bus rapid transit system Metrobús in Mexico City

Abstract

In this paper, we analyze a massive dataset with registers of the movement of vehicles in the bus rapid transit system Metrobús in Mexico City from February 2020 to April 2021. With these records and a division of the system into 214 geographical regions (segments), we characterize the vehicles’ activity through the statistical analysis of speeds in each zone. We use the Kullback–Leibler distance to compare the movement of vehicles in each segment and its evolution. The results for the dynamics in different zones are represented as a network where nodes define segments of the system Metrobús and edges describe similarity in the activity of vehicles. Community detection algorithms in this network allow the identification of patterns considering different levels of similarity in the distribution of speeds providing a framework for unsupervised classification of the movement of vehicles. The methods developed in this research are general and can be implemented to describe the activity of different transportation systems with detailed records of the movement of users or vehicles.

Stability of the Two-Link Metrobus

“…The stability of traffic refers to properties of the automatic transport vehicles (ATV), which do not have a strict certainty in terminology, requirements, indicators and methods of assessment [11][12][13][14][15][16]. In practice, experimental characteristics that determine the stability of the ATV while driving have been used and in theory -direct and indirect indicators and their dependencies, among which the critical speed of traffic is the main one.…”

Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

“…Also, it is important to emphasize that D KL (P||Q) is not a distance in the sense of a metric since the distance between P and Q is not necessarily the same as between Q and P. Also, from the definition in equation ( 9), it is clear that D KL (P||Q) > 0 and is null when P = Q. The Kullback-Leibler divergence is widely used in data science [82] but also in physical sciences and engineering, for example in the context of turbulence [83], the characterization of dynamical systems [84], or to compare the activity of vehicles in a transportation system [85], just to mention a few examples.…”