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2020-11-05
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Copyright (c) 2020 Arefeh Nasri, Hannah Younes, Lei Zhang
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Analysis of the effect of multi-level urban form on bikeshare demand: Evidence from seven large metropolitan areas in the United States
Arefeh Nasri
University of Maryland, College Park
Hannah Younes
University of Maryland, College Park
Lei Zhang
University of Maryland, College Park
DOI: https://doi.org/10.5198/jtlu.2020.1615
Keywords: Bikeshare, Urban Form, Multi-level Built Environment, Demand Analysis, Travel Behavior, Mixed Effect Modeling
Abstract
Bikeshare programs in their current form have been in place for several years in many cities across the United States. Encouraging people to use bikeshare for their daily routine travel has numerous social, economic, environmental, and health benefits. Therefore, it is important to understand factors influencing bikeshare use in different urban areas to improve the system and encourage more use. This paper investigates how the built environment at both local and regional scales influences bikeshare use in seven large metropolitan areas in the U.S. The study areas include Boston, Chicago, Philadelphia, Minneapolis, San Francisco, San Jose, and Washington, D.C., and the data consists of about 12 million bike trips from approximately 2,000 stations over a one-year period. In addition to linear regression models built for each individual city for comparison purposes, a multi-level mixed effect regression model is built to predict the number of trips originated from each station with respect to the local and regional built environment pattern. The results are consistent with previous research on the effect of land use at the local level on bikeshare demand and show that residential density, regional diversity, pedestrian-oriented road network density, and job accessibility via transit all have a significant positive effect on bikeshare demand. At the regional level, results suggest that the overall level of mixed-use development and overall bike-friendliness in the region (i.e., exclusive bike routes, right-of-way, and bike facilities) and higher congestion level in the region are significant factors influencing bikeshare activities and demand. Models developed in this study could be applied to other communities that are seeking to improve and/or expand their bikeshare systems, as well as cities planning to launch new bikeshare programs.
References
Bachand-Marleau, J., Lee, B., & El-Geneidy, A. (2012). Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record: Journal of the Transportation Research Board, 2314, 66–71.
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., ... & Scheipl, F. (2012). Package ‘lme4’. CRAN. R Foundation for Statistical Computing, Vienna, Austria.
Biehl, A., Ermagun, A., & Stathopoulos, A. (2018). Community mobility MAUP-ing: A socio-spatial investigation of bikeshare demand in Chicago. Journal of Transport Geography, 66, 80–90.
Buck, D., & Buehler, R. (2012). Bike lanes and other determinants of capital bikeshare trips. Paper presented at the 91st Transportation Research Board annual meeting, Washington, DC.
Buehler, R. (2011). Determinants of transport mode choice: A comparison of Germany and the USA. Journal of Transport Geography, 19(4), 644–657.
Christian, H. E., Bull, F. C., Middleton, N. J., Knuiman, M. W., Divitini, M. L., Hooper, P., & Giles-Corti, B. (2011). How important is the land-use mix measure in understanding walking behavior? Results from the RESIDE study. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 55.
Daddio, D. W. (2012). Maximizing bicycle sharing: An empirical analysis of capital bikeshare usage (Doctoral dissertation). University of North Carolina, Chapel Hill, NC.
DeMaio, P. (2009). Bike-sharing: History, impacts, models of provision, and future. Journal of Public Transportation, 12(4), 3.
Ding, C., Wang, D., Liu, C., Zhang, Y., & Yang, J. (2017). Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice, 100, 65–80.
El-Assi, W., Mahmoud, M. S., & Habib, K. N. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44(3), 589–613.
Faghih-Imani, A., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314.
Fishman, E. (2016). Bikeshare: A review of recent literature. Transport Reviews, 36(1), 92–113.
Frank, L. D., Schmid, T. L., Sallis, J. F., Chapman, J., & Saelens, B. E. (2005). Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine, 28(2), 117–125.
Gebhart, K., & Noland, R. B. (2014). The impact of weather conditions on bikeshare trips in Washington, DC. Transportation, 41(6), 1205–1225.
Gehrke, S. R., & Welch, T. F. (2019). A bikeshare station area typology to forecast the station-level ridership of system expansion. Journal of Transport and Land Use, 12(1), 221–235.
Guan, C., Srinivasan, S., & Nielsen, C. P. (2019). Does neighborhood form influence low-carbon transportation in China? Transportation Research Part D: Transport and Environment, 67, 406–420.
Hampshire, R. C., & Marla, L. (2012). An analysis of bike sharing usage: Explaining trip generation and attraction from observed demand. Paper presented at the 91st Annual meeting of the Transportation Research Board, Washington, DC.
Jäppinen, S., Toivonen, T., & Salonen, M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach. Applied Geography, 43, 13–24.
Kuzmyak, J. R., Walters, J., Bradley, M., & Kockelman, K. M. (2014). Estimating bicycling and walking for planning and project development: A guidebook (NCHRP Report 770). Washington, DC: National Cooperative Highway Research Program.
Le, H. T., Buehler, R., & Hankey, S. (2019). Have walking and bicycling increased in the US? A 13-year longitudinal analysis of traffic counts from 13 metropolitan areas. Transportation Research Part D: Transport and Environment, 69, 329–345.
Ma, T., Liu, C., & Erdoğan, S. (2015). Bicycle sharing and public transit: does Capital Bikeshare affect Metrorail ridership in Washington, D C? Transportation Research Record, 2534(1), 1–9
Martin, E. W., & Shaheen, S. A. (2014). Evaluating public transit modal shift dynamics in response to bikesharing: A tale of two US cities. Journal of Transport Geography, 41, 315–324.
Mateo-Babiano, I., Bean, R., Corcoran, J., & Pojani, D. (2016). How does our natural and built environment affect the use of bicycle sharing? Transportation Research Part A: Policy and Practice, 94, 295–307.
McNeil, N., Broach, J., & Dill, J. (2018). Breaking barriers to bike share: Lessons on bike share equity. ITE Journal, 88(2), 31–35.
Morency, C., Tr.panier, M., Faucher, J., P.ez, A., & Verreault, H. (2017). Modelling bikesharing usage in Montreal over 6 years (No. CIRRELT-2017-33). Montreal, Canada: Interuniversity Research Center on Enterprise Networks, Logistics and Transportation.
Muarer, L. K. (2011). Suitability study for a bicycle sharing program in Sacramento, California. Chapel Hill, NC: University of North Carolina at Chapel Hill, Department of City and Regional Planning.
Naess, P. (2011). ‘New urbanism’ or metropolitan-level centralization? A comparison of the influences of metropolitan-level and neighborhood-level urban form characteristics on travel behavior. Journal of Transport and Land Use, 4(1), 25–44.
Nair, R., Miller-Hooks, E., Hampshire, R. C., & Bušić, A. (2013). Large-scale vehicle sharing systems: Analysis of V.lib'. International Journal of Sustainable Transportation, 7(1), 85–106.
Nasri, A. A. (2016). The influence of urban form at different geographical scales on travel behavior: Evidence from US cities (Doctoral dissertation). University of Maryland, College Park, MD.
Nasri, A., & Zhang, L. (2012). Impact of metropolitan-level built environment on travel behavior. Transportation Research Record: Journal of the Transportation Research Board, 2323, 75–79.
Nasri, A., & Zhang, L. (2014). Assessing the impact of metropolitan-level, county-level, and local-level built environment on travel behavior: Evidence from 19 US urban areas. Journal of Urban Planning and Development, 141(3), 04014031.
Nasri, A., & Zhang, L. (2019). Multi-level urban form and commuting mode share in rail station areas across the United States: A seemingly unrelated regression approach. Transport Policy, 81, 311–319.
Nielsen, T. A. S., & Skov-Petersen, H. (2018). Bikeability–Urban structures supporting cycling. Effects of local, urban and regional scale urban form factors on cycling from home and workplace locations in Denmark. Journal of Transport Geography, 69, 36–44.
Parkin, J., Wardman, M., & Page, M. (2008). Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation, 35(1), 93–109.
Rajamani, J., Bhat, C. R., Handy, S., Knaap, G., & Song, Y. (2003). Assessing impact of urban form measures on nonwork trip mode choice after controlling for demographic and level-of-service effects. Transportation Research Record, 1831(1), 158–165.
Ranaiefar, F., & Rixey, R. A. (2016). Bike sharing ridership forecast using structural equation modeling. Paper presented at the Transportation Research Board 95th Annual Meeting (No. 16-6573), Washington, DC.
Rixey, R. (2013). Station-level forecasting of bikesharing ridership: Station network effects in three US systems. Transportation Research Record: Journal of the Transportation Research Board, 2387, 46–55.
Rodríguez, D. A., & Joo, J. (2004). The relationship between non-motorized mode choice and the local physical environment. Transportation Research Part D: Transport and Environment, 9(2), 151–173.
Schrank, D., & Lomax, T. (2007). The 2007 urban mobility report. College Station, TX: Transportation Institute, Texas A&M University.
Shaheen, S., Cohen, A., & Martin, E. (2013). Public bikesharing in North America: Early operator understanding and emerging trends. Transportation Research Record: Journal of the Transportation Research Board, 2387, 83–92.
Shaheen, S., Guzman, S., & Zhang, H. (2010). Bikesharing in Europe, the Americas, and Asia: past, present, and future. Transportation Research Record: Journal of the Transportation Research Board, 2143, 159-167.
Team, R. C. (2012). R: A language and environment for statistical computing. 2012. Vienna, Austria: R Foundation for Statistical Computing, 10.
Tingting, L., Li, L., & Wei, L. (2011). Analysis of public bicycles' impact on residents' trip chain. In Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE) (pp. 182-185). IEEE.
Tran, T. D., Ovtracht, N., & d’Arcier, B. F. (2015). Modeling bike sharing system using built environment factors. Procedia CIRP, 30, 293–298.
Wang, X., Lindsey, G., Schoner, J. E., & Harrison, A. (2016). Modeling bike share station activity: Effects of nearby businesses and jobs on trips to and from stations. Journal of Urban Planning and Development, 142(1), 04015001.
Weinberger, R., & Goetzke, F. (2019). Automobile ownership and mode choice: Learned or instrumentally rational? Travel Behavior and Society, 16, 153–160.
Younes, H., Nasri, A., Baiocchi, G., & Zhang, L. (2019). How transit service closures influence bikesharing demand: Lessons learned from SafeTrack project in Washington, DC metropolitan area. Journal of Transport Geography, 76, 83–92.
Younes, H., Zou, Z., Wu, J., & Baiocchi, G. (2020). Comparing the temporal determinants of dockless scooter-share and station-based bike-share in Washington, DC. Transportation Research Part A: Policy and Practice, 134, 308–320.
Zhang, L., Hong, J., Nasri, A., & Shen, Q. (2012). How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities. Journal of Transport and Land Use, 5(3), 40–52.
