Date Published: March 6, 2019
Publisher: Public Library of Science
Author(s): D. Loaiza-Monsalve, A. P. Riascos, Yanyong Guo.
The understanding of human mobility patterns in different transportation modes is an interdisciplinary research field with a direct impact in aspects as varied as urban planning, traffic optimization, sustainability, the reduction of operating costs as well as the mitigation of pollution in urban areas. In this paper, we study the global activity of users in bike-sharing systems operating in the cities of Chicago and New York. For this transportation mode, we explore the temporal and spatial characteristics of the mobility of cyclists. In particular, through the analysis of origin-destination matrices, we characterize the spatial structure of the displacements of users. We apply a mobility model for the global activity of the system that classifies the displacements between stations in local and non-local transitions. In local transitions, cyclists move in a region around each station whereas, in the non-local case, bike users travel with long-range displacements in a similar way to Lévy flights. We reproduce the spatial dynamics by using Monte Carlo simulations. The obtained results are similar to the observed in real data and reveal that the model implemented captures important characteristics of the global spatial dynamics in the systems analyzed.
With a high proportion of the world’s population living in cities, the understanding of patterns in human mobility in urban settlements, as well as the development of models that capture fundamental aspects of these systems from different perspectives, have become of utmost importance [1–5]. Individuals move in cities with different intentions, to buy or sell goods, to work, to meet other people, among a series of human activities that require intra-city displacements. In fact, good quality of life in a city requires adequate transport infrastructures . In order to satisfy the needs of their inhabitants, large cities have grown developing several public transportation modes like taxis [6–8], metro , bus services , bicycle-sharing systems, among others . Each of these systems operates with particular infrastructures and efficient displacements require the coupling between different transportation modes [11–13].
In this paper, we explore the global activity of users in bicycle-sharing systems. We analyze real data for users’ trips in the systems Divvy in Chicago and Citi Bike in New York. The datasets include massive records about start and end stations, start and end time of trips, trip duration, among other quantities like user types, age and gender information for registered members. As a first result, we study the temporal activity of users and we observe the same patterns for weekly activity as well as a bursty behavior in the time elapsed in the displacements between stations with probabilities that decay with the same inverse power law in the two BSS explored. In the second part, we analyze the distances between stations traveled by each of the users on their trips. We calculate origin-destination matrices for stations with high activity and with this information we obtain the probability of transition of users as a function of the distance between departure and arrival locations. Our results clearly reveal the same characteristics for the global dynamics in BSS classified them as local and long-range transitions. In local displacements, the users travel to stations around R ≈ 1Km from the departure station. In this case, the probability to pass to one of the stations in the local neighborhood is approximately constant. On the other hand, long-range transitions appear for users with displacements to stations beyond the local neighborhood and, in this case, the probabilities of transition decay with the distance in the same way as in the gravity-law model for human mobility.