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022 _a07339488
040 _aMSU
_bEnglish
_cMSU
_erda
050 0 0 _aHT169 JOU
100 1 _aWang, Xize
_eauthor
245 1 0 _aModeling bike share station activity :
_beffects of nearby businesses and jobs on trips to and from stations/
_ccreated by Xize Wanga, Greg Lindseyb, Jessica E. Schonerc and Andrew Harrison
264 1 _aReston :
_bASCE,
_c2016.
336 _2rdacontent
_atext
_btxt
337 _2rdamedia
_aunmediated
_bn
338 _2rdacarrier
_avolume
_bnc
440 _aJournal of urban planning and development
_vVolume 142, number 1
520 3 _aThe purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in the Minneapolis–St. Paul Metropolitan Area in Minnesota. The number of trips to and from each of the 116 bike share stations operating in 2011 was obtained from Nice Ride Minnesota. Data for independent variables included in the proposed models come from a variety of sources, including the 2010 U.S. Census; the Metropolitan Council, a regional planning agency; and the Cities of Minneapolis and St. Paul. Log-linear and negative binomial regression models are used to evaluate the marginal effects of these factors on average daily station trips. The models have high goodness of fit, and each of 13 independent variables is significant at the 10% level or higher. The number of trips at Nice Ride stations is associated with neighborhood sociodemographics (i.e., age and race), proximity to the central business district, proximity to water, accessibility to trails, distance to other bike share stations, and measures of economic activity. Analysts can use these results to optimize bike share operations, locate new stations, and evaluate the potential of new bike share programs.
650 _aBike-share
_vStation activity
_xBusiness
700 1 _aLindsey, Greg
_eco author
700 1 _aSchoner, Jessica E.
_eco author
700 1 _aHarrison, Andrew
_eco author
856 _uhttps://doi.org/10.1061/(ASCE)UP.1943-5444.0000273
942 _2lcc
_cJA
999 _c167160
_d167160