Bicycle Ridership Using Crowdsourced Data: Ordered Probit Model Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
Cycling is a healthier and greener travel mode that city planners and policymakers have encouraged for short-distance trips. Because cycling provides an efficient way to improve public health and reduce energy consumption, analyzing the contributing factors to bicycle usage on roadway segments is essential to quantify the impact of certain attributes on bicycle volume and to further provide a better cycling environment for cyclists to encourage nonmotorized travel. To gain a better understanding of the attributes that have a significant impact on cycling, this study collects crowdsourced bicycle data from Strava and combines the data with other supporting data, such as road characteristics, demographic information, temporal factors, geometry features, and bike facilities. An ordered probit model is then developed to analyze Strava users’ bicycle usage on each road segment in the city of Charlotte, North Carolina. The results reveal that road segment length, number of through lanes, median household income, total households in a census block, cycling on a suggested bike route, greenway, US route, and one-way road all have a positive impact on Strava user counts on a road segment from 6 a.m. to 6 p.m. Conversely, the variables for cycling on weekdays, total families in a census block, slope, signed bike routes, and suggested bike routes with low comfort have a negative impact on the Strava user counts on a road segment. Based on the modeling results, recommendations are also made to assist in improving the cycling environment and increasing future bicycle volume.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all of the data, models, or code used during the study were provided by a third party (Strava) and, thus, are proprietary or confidential. Direct requests for these materials may be made to the provider.
Acknowledgments
The authors want to express their deepest gratitude to the financial support from the United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at the University of North Carolina at Charlotte (Grant No. 69A3551747133).
References
Ben-Akiva, M., and M. Bierlaire. 1999. “Discrete choice methods and their applications to short term travel decisions.” In Handbook of transportation science, edited by R. Hall, 5–34. Norwell, MA: Kluwer Academic Publishers.
Boes, S., and R. Winkelmann. 2006. “Ordered response models.” Allgemeines Statistisches Archiv. 90 (1): 167–181. https://doi.org/10.1007/s10182-006-0228-y.
Boss, D., T. Nelson, M. Winters, and C. J. Ferster. 2018. “Using crowdsourced data to monitor change in spatial patterns of bicycle ridership.” J. Transp. Health 9 (Jun): 226–233. https://doi.org/10.1016/j.jth.2018.02.008.
Boulos, M. N. K., B. Resch, D. N. Crowley, J. G. Breslin, G. Sohn, R. Burtner, W. A. Pike, E. Jezierski, and K. Y. S. Chuang. 2011. “Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: Trends, OGC standards and application examples.” Int. J. Health Geographics 10 (1): 67. https://doi.org/10.1186/1476-072X-10-67.
Bovy, P., and S. Fiorenzo-Catalano. 2007. “Stochastic route choice set generation: Behavioral and probabilistic foundations.” Transportmetrica 3 (3): 173–189. https://doi.org/10.1080/18128600708685672.
Brabham, D. C. 2008. “Crowdsourcing as a model for problem solving: An introduction and cases.” Convergence 14 (1): 75–90. https://doi.org/10.1177/1354856507084420.
Brabham, D. C. 2012. “Motivations for participation in a crowdsourcing application to improve public engagement in transit planning.” J. Appl. Commun. Res. 40 (3): 307–328. https://doi.org/10.1080/00909882.2012.693940.
Broach, J., J. Dill, and J. Gliebe. 2012. “Where do cyclists ride? A route choice model developed with revealed preference GPS data.” Transp. Res. Part A: Policy Pract. 46 (10): 1730–1740. https://doi.org/10.1016/j.tra.2012.07.005.
Broach, J., J. Gliebe, and J. Dill. 2010. “Calibrated labeling method for generating bicyclist route choice sets incorporating unbiased attribute variation.” Transp. Res. Rec. 2197 (1): 89–97. https://doi.org/10.3141/2197-11.
Casello, J., and V. Usyukov. 2014. “Modeling cyclists’ route choice based on GPS data.” Transp. Res. Rec. 2430 (1): 155–161. https://doi.org/10.3141/2430-16.
Charlotte Bicycle Map. 2017. “Bicycle program.” Accessed September 7, 2018. https://charlotte.maps.arcgis.com/apps/PanelsLegend/index.html?appid=00e8015ea3e54607a880fe31cc7e2fbf.
Charlton, B., J. Hood, E. Sall, and M. A. Schwartz. 2011. “Bicycle route choice data collection using GPS-enabled smartphones.” In Proc., Transportation Research Board 90th Annual Meeting. Washington, DC: Transportation Research Board.
City of Charlotte DOT. 2017. “Charlotte BIKES bicycle plan.” Accessed September 7, 2018. https://charlottenc.gov/Transportation/Programs/Documents/Charlotte%20BIKES%20Final.pdf.
Dill, J., and J. Gliebe. 2008. Understanding and measuring bicycling behavior: A focus on travel time and route choice. Portland, OR: Oregon Transportation Research.
Enriched Charlotte Blocks. 2017. “My map.” Accessed September 7, 2018. http://www.arcgis.com/home/webmap/viewer.html?url=https://services1.arcgis.com/yfahUFAYAdeS5rmM/ArcGIS/rest/services/Enriched%20Enriched%20Charlotte%20Blocks/FeatureServer&source=sd.
Figliozzi, M. A., and B. P. Blanc. 2015. Evaluating the use of crowdsourcing as a data collection method for bicycle performance measures and identification of facility improvement needs. Portland: Portland State Univ.
Fosgerau, M., E. Frejinger, and A. Karlstrom. 2013. “A link based network route choice model with unrestricted choice set.” Transp. Res. Part B: Methodol. 56 (Oct): 70–80. https://doi.org/10.1016/j.trb.2013.07.012.
Grond, K. 2016. Route choice modeling of cyclists in Toronto. DissCanada: Univ. of Toronto.
Hochmair, H. H., E. Bardin, and A. Ahmouda. 2019. “Estimating bicycle trip volume for Miami-Dade county from Strava tracking data.” J. Transp. Geogr. 75 (Feb): 58–69. https://doi.org/10.1016/j.jtrangeo.2019.01.013.
Hood, J., E. Sall, and B. Charlton. 2011. “A GPS-based bicycle route choice model for San Francisco, California.” Transp. Lett. 3 (1): 63–75. https://doi.org/10.3328/TL.2011.03.01.63-75.
Howe, J. 2006. “The rise of crowdsourcing.” Wired magazine 14 (6): 1–4.
Khatri, R., C. R. Cherry, S. S. Nambisan, and L. D. Han. 2016. “Modeling route choice of utilitarian bikeshare users with GPS data.” Transp. Res. Rec. 2587 (1): 141–149. https://doi.org/10.3141/2587-17.
Koppelman, F. S., and C. Bhat. 2006. A self- instructing course in mode choice modeling: Multinomial and nested logit models. Washington, DC: Federal Transit Administration.
LaMondia, J., and K. Watkins. 2017. Using crowdsourcing to prioritize bicycle route network improvements, Pasadena, CA: Southeastern Transportation Research, Innovation, Development and Education Center.
Lidar2017_Slope. 2017. “ArcGIS online.” Accessed September 7, 2018. https://mecklenburgcounty.exavault.com/share/view/1dxv7-b5jh2qyy/Surface%20Data.
Martí, I. G., L. E. Rodríguez, M. Benedito, S. Trilles, A. Beltrán, L. Díaz, and J. Huerta. 2012. “Mobile application for noise pollution monitoring through gamification techniques.” In Proc., 11th Int. Conf. on Entertainment Computing, 562–571. Berlin: Springer.
Misra, A., A. Gooze, K. Watkins, M. Asad, and C. A. Le Dantec. 2014. “Crowdsourcing and its application to transportation data collection and management.” Transp. Res. Rec. 2414 (1): 1–8. https://doi.org/10.3141/2414-01.
Moore, M. 2015. “Modeling factors influencing commuter cycling routes: A study of GPS cycling records in Auburn, Alabama.” M.Sc. thesis, Dept. of Civil Engineering, Auburn Univ.
Musakwa, W., and K. M. Selala. 2016. “Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa.” Data Brief 9 (Dec): 898–905. https://doi.org/10.1016/j.dib.2016.11.002.
Nassir, N., J. Ziebarth, E. Sall, and L. Zorn. 2014. “Choice set generation algorithm suitable for measuring route choice accessibility.” Transp. Res. Rec. 2430 (1): 170–181. https://doi.org/10.3141/2430-18.
Overeem, A., J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, and R. Uijlenhoet. 2013. “Crowdsourcing urban air temperatures from smartphone battery temperatures.” Geophys. Res. Lett. 40 (15): 4081–4085. https://doi.org/10.1002/grl.50786.
Papinski, D., and D. M. Scott. 2011. “A GIS-based toolkit for route choice analysis.” J. Transp. Geogr. 19 (3): 434–442. https://doi.org/10.1016/j.jtrangeo.2010.09.009.
Shilton, A. C. and the bicycling magazine editors. 2018. “The best bike cities in America.” Accessed September 3, 2019. https://www.bicycling.com/culture/a23676188/best-bike-cities-2018/.
Strava Metro. 2018. “Strava global heat map.” Accessed September 11, 2019. https://www.strava.com/heatmap#3.90/-88.71779/36.80523/hot/ride.
The League of American Bicyclists. 2018. “Bicycling and walking in the United States: 2018 benchmarking report.” Accessed September 11, 2019. https://bikeleague.org/benchmarking-report.
Train, K. E. 2009. Discrete choice methods with simulation. Cambridge, UK: Cambridge University Press.
Whitla, P. 2009. “Crowdsourcing and its application in marketing activities.” Contemporary Manage. Res. 5 (1): 15–28. https://doi.org/10.7903/cmr.1145.
Yeboah, G., and S. Alvanides. 2015. “Route choice analysis of urban cycling behaviors using OpenStreetMap: Evidence from a British urban environment.” In OpenStreetMap in GIScience, 189–210. Berlin: Springer.
Zimmermann, M., T. Mai, and E. Frejinger. 2017. “Bike route choice modeling using GPS data without choice sets of paths.” Transp. Res. Part C: Emerg. Technol. 75 (Feb): 183–196. https://doi.org/10.1016/j.trc.2016.12.009.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: May 28, 2019
Accepted: Mar 9, 2020
Published online: May 31, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 31, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.