Bicycle cycles and mobility patterns - Exploring and characterizing data from a community bicycle program

📝 Abstract

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.

💡 Analysis

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.

📄 Content

Bicycle cycles and mobility patterns Exploring and characterizing data from a community bicycle program Andreas Kaltenbrunner andreas.kaltenbrunner@barcelonamedia.org Rodrigo Meza rodrigo.meza@barcelonamedia.org Jens Grivolla jens.grivolla@barcelonamedia.org Joan Codina joan.codina@barcelonamedia.org Rafael Banchs rafael.banchs@barcelonamedia.org Barcelona Media Centre d’Innovació, Ocata 1, 08003 Barcelona, Spain ABSTRACT This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the sta- tions of the community bicycle program Bicing in Barcelona. The data was obtained by periodic mining of a KML-file ac- cessible 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 min- utes/hours. Furthermore a model for identifying the most probable routes between stations is briefly sketched. Categories and Subject Descriptors G.3 [Probability and statistics]: Time series analysis; H.3.3 [information storage and Retrieval]: Information Search and Retrieval—Clustering, Information filtering General Terms Measurement Keywords Mobility pattern, community bicycle program, urban behav- ior 1. INTRODUCTION Human mobility patterns have received a certain amount of attention in recent studies. However, it is not a straightfor- ward task to obtain data which allows a large scale study, mostly due to privacy issues. Notable exceptions where the authors were able to overcome those difficulties include the use of geotagged photos [3] and location data of mobile phones [9, 4], or analyzing the circulation of individual ban- knotes [2] and civil aviation traffic [7] to reconstruct geo- spatial data of human displacements in different distance- scales. Some of these studies focus on the trajectories of individ- uals which are reconstructed in several different manners. Large distance displacements can be deduced from aviation traffic and have then been applied to predict the spread of infectious diseases [7]. Another quite ingenious way of the same authors to interfere middle and large scale trajectories was analyzing the circulation data of banknotes provided by individual users at an online bill-tracking system [2]. This study showed that human travel distances can be described by a two-parameter continuous-time random walk model. Shorter distances have been analyzed in great detail in [4], using position data of individual mobile users. The authors showed that individuals follow simple and reproducible pat- terns of mobility in their everyday displacements, a fact that has not been found in [2] for middle and large scale trajec- tories. Another type of short distance patterns have been analyzed in [3], where the focus changed from everyday life patterns to the behavior of tourists in foreign cities. Their spatio-temporal data was deduced from geo-referenced pho- tos and the obtained results where contrasted with mobile phone usage. A case where, on the contrary to the above described stud- ies, only aggregate spatio temporal data is available (e. g. the number of persons at time x in place y), which does not allow the identification of individual trajectories, was ana- lyzed in a recent study [9]. Data of aggregate mobile phone usage allowed to construct activity cycles for different loca- tions, with clear differences between working day and week- end patterns as well as a characterization of certain areas within the city by a cluster analysis. Here we perform a similar study using a different type of aggregate data to infer human mobility patterns. The input spatio temporal data, which has been obtained by a web mining process, is the number of bicycles in the approxi- mately 400 different stations of Barcelona’s community bi- cycle program Bicing. To our knowledge this is the first study using this type of mobility data. arXiv:0810.4187v1 [cs.CY] 23 Oct 2008 The aims of this study are twofold. First, we want to ob- tain a description of the general patterns and activity cycles, which can be deduced from this type of data and second, we want to check if knowledge of those patterns can lead to a prediction of future behavior, which would allow to im- prove the current web-service of bicing and in turn increase users satisfaction with the system. Knowledge of those pat- terns could lead to an optimization of the bicing system itself, allowing the operator to predict shortage or overflow of bicycles in certain stations well in advance and adapt its redistribution schedule accordingly on the fly. Prediction of Bicing activity is a problem related to traffic congestion control, which has been analyzed traditionally for vehicular traffic. See for examp

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