Bikeshare trip generation in New York City |
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Institution: | 1. Department of Civil Engineering and Applied Mechanics, McGill University, Canada;2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, United States;1. Department of Civil Engineering and Applied Mechanics, McGill University, Canada;2. Transportation Research Institute, University of Michigan, United States;3. Department of Industrial and Systems Engineering, University of Illinois at Urbana-Champaign, United States;4. Department of Civil, Environmental and Construction Engineering, University of Central Florida, United States;1. Department of Civil Engineering and Applied Mechanics, McGill University, Canada;2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, United States;1. School of Transportation, Southeast University, Nanjing 210096, China;2. Department of City and Regional Planning, University of North Carolina at Chapel Hill, NC 27599, United States;3. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, United States |
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Abstract: | Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. We estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). We find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. Our findings are nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. We use our models, based on 2014 data, to forecast the trips generated at new stations opened in 2015. Results suggest there is large variation in predictive power, partly caused by variation in weather, but also by other factors that cannot be predicted. This leads us to the conclusion that the nuances we find in our inferential analysis are more useful for transportation planners. |
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Keywords: | Bikeshare Trip generation Count data Spatial correlation Negative binomial Bicycling |
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