Analyzing the usage patterns of electric bicycles

We analyze over 100 Gb of electric bicycle (e-bike) usage data collected through the University of Waterloo WeBike field trial. The WeBike fleet consists of 31 instrumented e-bikes used by University of Waterloo faculty, staff and students. We break down usage and battery charging habits by gender and by occupation, and we compare participants' initial estimates of how much they thought they would ride their e-bike with their actual riding histories. We also discuss data pre-processing challenges such as identifying trip start and end times from noisy and incomplete sensing data.

[1]  Hermann de Meer,et al.  Range prediction for electric bicycles , 2016, e-Energy.

[2]  Florian Michahelles,et al.  Inferring usage characteristics of electric bicycles from position information , 2010, LocWeb '10.

[3]  Susan L Handy,et al.  Experiences of electric bicycle users in the Sacramento, California area , 2014 .

[4]  Eui-Hwan Chung,et al.  A Trip Reconstruction Tool for GPS-based Personal Travel Surveys , 2005 .

[5]  Marco Dozza,et al.  Using naturalistic data to assess e-cyclist behavior , 2016 .

[6]  Mette Møller,et al.  Age and attitude: Changes in cycling patterns of different e-bike user segments , 2016 .

[7]  Hassan A. Karimi,et al.  A pedestrian network construction algorithm based on multiple GPS traces , 2013 .

[8]  G. Gremion,et al.  Electric bicycles as a new active transportation modality to promote health. , 2011, Medicine and science in sports and exercise.

[9]  J-S Kim NODE BASED MAP MATCHING ALGORITHM FOR CAR NAVIGATION SYSTEM , 1996 .

[10]  Christopher R. Cherry,et al.  Use characteristics and mode choice behavior of electric bike users in China , 2007 .

[11]  João Peças Lopes,et al.  Electric vehicle integration into modern power networks , 2013 .

[12]  Carpenter Tommy,et al.  Measuring & Mitigating Electric Vehicle Adoption Barriers , 2015 .

[13]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[14]  Florin Mariasiu,et al.  Electric vehicle battery technologies: From present state to future systems , 2015 .

[15]  Vladimir Usyukov,et al.  Development of a Cyclists' Route-Choice Model: An Ontario Case Study , 2013 .

[16]  Josef F. Krems,et al.  The German Naturalistic Cycling Study – Comparing cycling speed of riders of different e-bikes and conventional bicycles , 2014 .

[17]  Marco Dozza,et al.  Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012) , 2014, IEEE Transactions on Intelligent Transportation Systems.

[18]  G. Rose E-bikes and Transportation Policy: Insights from Early Adopters , 2012 .

[19]  Stefan Schönfelder,et al.  Eighty Weeks of Global Positioning System Traces: Approaches to Enriching Trip Information , 2004 .

[20]  Lin Sun,et al.  iBOAT: Isolation-Based Online Anomalous Trajectory Detection , 2013, IEEE Transactions on Intelligent Transportation Systems.

[21]  Brian Casey Langford,et al.  Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders. , 2015, Accident; analysis and prevention.

[22]  Kang An,et al.  Travel Characteristics of E-bike Users: Survey and Analysis in Shanghai☆ , 2013 .

[23]  Aslak Fyhri,et al.  Effects of e-bikes on bicycle use and mode share , 2015 .

[24]  Jie Gao,et al.  Persistence based online signal and trajectory simplification for mobile devices , 2014, SIGSPATIAL/GIS.

[25]  Grant A. Covic,et al.  Wireless Fleet Charging System for Electric Bicycles , 2015, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[26]  Kay W. Axhausen,et al.  Processing Raw Data from Global Positioning Systems without Additional Information , 2009 .