Examining spatio-temporal mobility patterns of bike-sharing systems: the case of BiciMAD (Madrid)

Abstract

Over the past decades, Bike-Sharing Systems have been implemented in hundreds of cities all around the world. During this time, numerous academic studies have been published with analyses from different perspectives. The aim of this study is to build upon this research by bringing together a spatial and a temporal analysis of the cycling flow of BiciMAD, the Madrid Bike-Sharing System. By combining over 21 million GPS records and various maps the study visually explores cycling mobility patterns across the city on three different temporal scales: over the course of the day, during working days or weekends and over the course of a whole year to provide a better understanding of the season-dependent demand. The study also reveals the most important flows between origin-destination pairs and uncovers the radically different spatio-temporal travel patterns of frequent users and occasional users.

Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities

“…Our results shed light on the influence of intersecting identities on the trip schedules, spatial preferences and travel speed of bike-share users and e-scooter riders navigating through Barcelona streets. In terms of timing, we found that bike-share and e-scooter trips overlap with commuting trips, a pattern also observed in the micromobility schemes of other cities (Li et al, 2020;Talavera-Garcia et al, 2021). Our findings, however, show that these modes are also highly used during the afternoon, especially by women, which might indicate a link between micromobility trips and non-work-related trip purposes.…”

Applying a Pedestrian Level of Service in the Context of Social Distancing: The Case of the City of Madrid

“…In recent years, there has been a revolution in the use of geolocation data [ 22 ] for different forms of transport, providing us with a better understanding of movements in cities [ 23 ]. In addition, these data from geolocated devices and sensors provide a valuable source of information for pandemic monitoring and control [ 24 ], allowing for the identification of spatial transmission [ 25 ].…”

Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

“…An important overarching issue is that one must of course recognize that subway and bus rides are not walking. The same is true of related analysis of taxi cab (Guo & Karimi, 2017 ; Castro et al, 2013 ) and bike share data-sets (Talavera-Garcia et al, 2021 ). Transit mobility is an accommodation to the problem of mass and collective transport of huge numbers of people; transit trips are therefore very different than customer journeys.…”