Human mobility in bike-sharing systems: Structure of local and non-local dynamics

RESEARCH ARTICLE Human mobility in bike-sharing systems: Structure of local and non-local dynamics D. Loaiza-Monsalve1☯, A. P. Riascos ID2☯* 1 Department of Civil Engineering, Universidad Mariana, San Juan de Pasto, Colombia, 2 Instituto de Fı́sica, Universidad Nacional Autónoma de México, Ciudad de México, México ☯ These authors contributed equally to this work. * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Loaiza-Monsalve D, Riascos AP (2019) Human mobility in bike-sharing systems: Structure of local and non-local dynamics. PLoS ONE 14(3): e0213106. https://doi.org/10.1371/journal. pone.0213106 Editor: Yanyong Guo, University of British Columbia, CANADA Received: November 14, 2018 Abstract 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. Accepted: February 14, 2019 Published: March 6, 2019 Copyright: © 2019 Loaiza-Monsalve, Riascos. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The datasets used in this study were obtained from Divvy (https://www. divvybikes.com/system-data) and Citi Bike websites (https://www.citibikenyc.com/systemdata). Funding: The authors acknowledge financial support from the Centro de Investigaciones (CEI), Universidad Mariana. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction 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 [1]. In order to satisfy the needs of their inhabitants, large cities have grown developing several public transportation modes like taxis [6–8], metro [9], bus services [10], bicycle-sharing systems, among others [1]. Each of these systems operates with particular infrastructures and efficient displacements require the coupling between different transportation modes [11–13]. Bike-sharing systems (BSS) have grown rapidly in the past decade. Although the concept has been around since the 1960s, the number of cities offering bike share has increased PLOS ONE | https://doi.org/10.1371/journal.pone.0213106 March 6, 2019 1 / 17 Human mobility in bike-sharing systems Competing interests: The authors have declared that no competing interests exist. significantly in the last two decades [14, 15]. The term bike-share system refers to all the infrastructure and provision of bikes in a system where users pick up and drop off bicycles at selfserving docking stations [14]. In comparison with other modes of transport, BSS offer a reliable, practical and sustainable transportation option for short to medium distance urban utilitarian and recreational trips [16]. In addition, it is widely accepted, cycling tends to produce health benefits and reduce air pollution and policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems [17]. The users’ movement characterization in BSS provides an important tool to study global human mobility behavior and, a deep understanding of the statistical patterns embedded in the bike flow data is an urgent and overriding issue to inform decision-making for a variety of problems including traffic prediction, station placement, and bike redistribution [18]. In addition, the detection of spatiotemporal patterns in these systems [16, 18–24] has impact in the implementation of re-balancing strategies [25], the reduction of costs [26], the study of the relation between the mobility of users and the spatial structure of urban areas [16, 27–30], among other benefits [31]. In this work, we analyze spatiotemporal patterns emerging in BSS in the cities of Chicago and New York. In the first part, we characterize the temporal dynamics of users. We find similar behaviors in the weekly activity of users of public bicycles in both systems. In addition, we identify an inverse power-law relation for the probability of the time that the cyclists spend on their trips. On the other hand, the geographical locations of stations in BSS remain the same for long periods of time or at least vary on a different scale from the daily activity of the system. This fact is important in the study of BSS and allows a correct description of the global activity in terms of origin-destination matrices. Through the analysis of probabilities of transition between stations, we identify the parameters of a model that associates a local neighborhood around each station, for which cyclists move to stations independently of their geographical separation, and long-range displacements with probabilities of transition that decay as an inverse power law of the distance between stations. We simulate the systems obtaining results that describe appropriately the real data and reveal that the model explored captures important aspects of the global dynamics in BSS. Our findings contribute to a better understanding of mobility patterns emerging in BSS. The methods developed in this research can be implemented in different existing bicycle-sharing systems to identify temporal and spatial patterns associated with human mobility in urban areas. Metho (...truncated)