| Resumen |
It is important to know the mobility in terms of the origin and destination of trips made by users of station-based bike-sharing systems (BSS), such as the ECOBICI system in Mexico City. The importance lies in the fact that it can help to understand the problem of lack of bikes and saturation of stations and therefore could partially help to solve this balancing problem (moving bikes to where they are needed). Mobility patterns are discovered in this paper using one and two-variable graphs (histograms, scatter plots, and box plots), tools used in the early stages of Data Science to describe a complex phenomenon in a simple but effective way. With the help of these graphs, temporal, and geographic patterns of urban bike mobility in Mexico City are described. This paper describes the following patterns: a) neighborhoods of origin and destination of bike trips; b) recurrent patterns on weekdays and weekends; c) patterns in different schedules; d) patterns between neighborhoods and e) neighborhoods where most of the trips are made locally. Neighborhoods with similar mobility have been identified, having a proportion of recurring trips at defined schedules throughout the day, with one or more increases in the demand for bikes. However, neighborhoods with abnormal mobility have also been found with a single schedule in which demand increases, as well as some relationships between some neighborhoods that regularly share trips. This is an example of the application of Data Science using data visualization tools applied to urban mobility and bike-sharing systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
