How Does Land-Use and Urban Form Impact Bicycle Flows--Evidence from the Bicycle-Sharing System (BIXI) in Montreal

Installed in 2009, BIXI was the first major public bicycle-sharing in Montreal, Canada. The BIXI system has been a success, accounting for more than one million trips annually. This success has increased the interest in exploring the factors affecting bicycle-sharing flows and usage. Using data compiled as minute by minute readings of bicycle availability at all the stations on the BIXI system between April and August 2012 this study contributes to literature on bicycle-sharing. The authors examine the influence of meteorological data, temporal characteristics, bicycle infrastructure, land use and built environment attributes on arrival and departure flows at the station level using a multilevel statistical model technique that is replicable for other regions to adopt. The findings allow the authors to identify factors contributing to increased usage of bicycle-sharing in Montreal and provide recommendations on station size and location decisions. The developed methodology and findings can be of benefit to city planners and engineers who are designing or modifying bicycle-sharing systems with the goal of maximizing usage and availability.

[1]  J. N. K. Rao,et al.  Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems: Comment , 1977 .

[2]  V. E. Daniel,et al.  Determinants of bicycle use: do municipal policies matter? , 2004 .

[3]  Jessica Schoner,et al.  Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations , 2016, 2207.10577.

[4]  Ahmed El-Geneidy,et al.  Breaking into Bicycle Theft: Insights from Montreal, Canada , 2015 .

[5]  Ahmed M El-Geneidy,et al.  Much-Anticipated Marriage of Cycling and Transit , 2011 .

[6]  Rafael E. Banchs,et al.  Article in Press Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System , 2022 .

[7]  R. Alexander Rixey,et al.  Station-Level Forecasting of Bikesharing Ridership , 2013 .

[8]  Céline Robardet,et al.  Characterizing the speed and paths of shared bicycles in Lyon , 2010, ArXiv.

[9]  Lucas J Carr,et al.  Validation of Walk Score for estimating access to walkable amenities , 2010, British Journal of Sports Medicine.

[10]  Céline Robardet,et al.  Shared Bicycles in a City: a Signal Processing and Data Analysis Perspective , 2011, Adv. Complex Syst..

[11]  Gregory R Krykewycz,et al.  Defining a Primary Market and Estimating Demand for Major Bicycle-Sharing Program in Philadelphia, Pennsylvania , 2010 .

[12]  Simon Washington,et al.  Bike Share: A Synthesis of the Literature , 2013 .

[13]  Dirk C. Mattfeld,et al.  Strategic and Operational Planning of Bike-Sharing Systems by Data Mining - A Case Study , 2011, ICCL.

[14]  P. DeMaio Bike-sharing: History, Impacts, Models of Provision, and Future , 2009 .

[15]  David Daddio Maximizing bicycle sharing: an empirical analysis of capital bikeshare usage , 2012 .

[16]  D. Harville Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems , 1977 .

[17]  Elise Miller-Hooks,et al.  Large-Scale Vehicle Sharing Systems: Analysis of Vélib' , 2013 .

[18]  Ralph Buehler,et al.  Bike Lanes and Other Determinants of Capital Bikeshare Trips , 2012 .

[19]  Ahmed M El-Geneidy,et al.  Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use , 2012 .

[20]  Daniel Fuller,et al.  Use of a new public bicycle share program in Montreal, Canada. , 2011, American journal of preventive medicine.

[21]  Nuria Oliver,et al.  Sensing and predicting the pulse of the city through shared bicycling , 2009, IJCAI 2009.