Spatial analysis of dynamic movements of Velo'v, Lyon's shared bicycle program (original) (raw)
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Examining spatio-temporal mobility patterns of bike-sharing systems: the case of BiciMAD (Madrid)
Journal of Maps
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.
Shared Bicycles in a City: A Signal Processing and Data Analysis Perspective
Advances in Complex Systems, 2011
Community shared bicycle systems, such as the Vélo'v program launched in Lyon in May 2005, are public transportation programs that can be studied as a complex system composed of interconnected stations that exchange bicycles. They generate digital footprints that reveal the activity in the city over time and space, making possible a quantitative analysis of movements using bicycles in the city. A careful study relying on nonstationary statistical modeling and data mining allows us to first model the time evolution of the dynamics of movements with Vélo'v, that is mostly cyclostationary over the week with nonstationary evolutions over larger time-scales, and second to disentangle the spatial patterns to understand and visualize the flows of Vélo'v bicycles in the city. This study gives insights on the social behaviors of the users of this intermodal transportation system, the objective being to help in designing and planning policy in urban transportation.
Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system
Understanding human mobility dynamics is of fundamental significance for many applications, and a wide range of data-driven mobility studies have been conducted using different datasets. Mobility traces which provide digital records of individual mobility allow analysis of individual mobility patterns, trends, and anomalies. Bicycle Sharing Systems (BSS) with origin-destination (OD) sensing systems that record departure and arrival times of each trip are among the most promising urban transport systems which do have such digital data available. BSS allow users to choose their own origin, route, and destination as well as travel time based on their needs. This flexibility leads to uncertainty on the operator side in terms of system use, and this thesis explores both uncertainty and regularity in demand to gain new insights for improving BSS deployments, services, and operations. Using BSS data from two cities (London and Washington DC), this thesis focuses on three main topics: station neighbourhood analysis, individual next place prediction, and prediction of system demand from system-level to individual station-level. Stations neighbourhood analysis aims to reveal the quality of connections among nearby stations by examining users' behaviour in choosing other stations when their commonly visited station is disturbed because it is of out of service (shutdown) or in an imbalanced state (full or empty). Two methods are proposed to conduct this analysis which are spatial-mobility-motifs and station temporary shutdown. Two metrics are also proposed to measure the quality of connections which are impact distance and usage transformation. Results show that 300 metres of travel distance is the impact distance of a station shutdown as measured by at least 20% usage change for nearby stations. 300 metres is also the most common distance that appears during motif analysis when users choose nearby stations within a neighbourhood. Results from these both analyses could be used to help BSS operators identify potentially ineffective stations and isolated stations. 300 metres can also be used as a standard distance between stations when deploying a new system or redesigning the existing network topology. User clustering aims to group users with similar mobility behaviour. Information theory is then used to measure the next-location predictability of each cluster. The goal is to identify highly predictable users so that useful services might be offered based on their predicted next place. Two temporal clustering metrics are proposed which are total trips (1 feature) and hourly trips across the day (24 features). These metrics adequately reflect the frequency and the regularity of user mobility. Three clusters are identified with distinct spatiotemporal characteristics which are named casual users, regular users, and commuters. Entropy analysis demonstrates that
The pulse of the cycling city: visualising Madrid bike share system GPS routes and cycling flow
Journal of Maps
With the aim of shifting towards a more sustainable urban transport model, cycling mobility is being promoted in many cities and, in consequence, Bike Share Systems have been the focus of attention in an increasing number of studies over the past years. However, we know very little about the impact of these BSS in cities beyond the station level. What paths do cyclists follow? What are the most important urban arteries in terms of cycling flow? These are important questions to be addressed in order to implement policies and infrastructure where they are really needed. The main goal of this study is to visualise the cycling flow derived from Madrid BSS activity, obtained by processing over 250,000 GPS routes, and to provide an analysis of how this flow is distributed across the urban street network at different moments. We explore the diverse levels of use over the course of the day, and during the weekdays, weekends or holidays, as well as the different cycling patterns of frequent and occasional users.
Under the background of sustainable development, many cities in the world have instituted public bicycle sharing system as a viable alternative mode of transportation to complement existing long-distance bus-and metro-transit systems. We make a statistical analysis of bicycle usage at each rental station and extracted trip chains based on one-month IC card data in order to measure the reasonability of the number and distribution of rental stations combined with surrounding land-use types and facilities. Some concepts such as centrality, connectivity and attractiveness are introduced to measure the scale of rental stations and assess their importance in this public bicycle system. Besides, with the visualized analysis of O-D trip-chain, the tidal phenomenon can be further confirmed to exist and also we can distinguish the type of rental stations in rush hours.
A novel spatio-temporal clustering technique to study the bike sharing system in Lyon
2020
In the last decades cities have been changing at an incredible rate growing the needs of efficient urban transportation to avoid traffic jams and high environmental pollution. In this context, bike sharing systems (BSSs) have been widely adopted by major regions like Europe, North America and Asia-Pacific becoming a common feature of all metropolitan areas. Its fast growing has increased the need of new monitoring and forecasting tools to take fast decisions and provide an efficient mobility management. In this context we focus on the BSS of Lyon in France, called Vélo’v. In particular we analyse a dataset containing the loading profiles of 345 bike stations over one week, treating the data as continuous functional observations over a period of one day. The aim of this work is to identify spatio-temporal patterns on the usage of bike sharing stations, identifying groups of stations and days with similar behaviour, with the purpose of providing useful information to the fleet manager...
2011
Community shared bicycle systems, such as the Vélo’v program launched in Lyon in May 2005, are public transportation programs that can be studied as a complex system composed of interconnected stations that exchange bicycles. They generate digital footprints that reveal the activity in the city over time and space, making possible a quantitative analysis of movements using bicycles in the city. A careful study relying on nonstationary statistical modeling and data mining allows us to first model the time evolution of the dynamics of movements with Vélo’v, that is mostly cyclostationary over the week with nonstationary evolutions over larger time-scales, and second to disentangle the spatial patterns to understand and visualize the flows of Vélo’v bicycles in the city. This study gives insights on the social behaviors of the users of this intermodal transportation system, the objective being to help in designing and planning policy in urban transportation.
Madrid cycle track: visualizing the cyclable city
Journal of Maps, 2015
Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day
Computing Research Repository, 2008
This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. The data was obtained by periodic mining of a KML-file accessible through the Bicing website. Although in principle very noisy, after some preprocessing and filtering steps the data allows to detect temporal patterns in mobility as well as identify residential, university, business and leisure areas of the city. The results lead to a proposal for an improvement of the bicing website, including a prediction of the number of available bikes in a certain station within the next minutes/hours. Furthermore a model for identifying the most probable routes between stations is briefly sketched.