Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations
Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations
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DOI：
10.1061/(asce)up.1943-5444.0000273
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Xize Wang;G. Lindsey;J. Schoner;Andrew Harrison
中科院分区：
文献类型：
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作者：
Xize Wang;G. Lindsey;J. Schoner;Andrew Harrison
北美多个城市已经建立了自行车共享系统。所有自行车共享计划的一个目标是最大限度地增加往返自行车共享站的出行次数。这项研究的目的是确定自行车站活动的相关性，特别强调往返自行车站的出行与附近企业和就业机会数量的关联。使用明尼阿波利斯-圣路易斯 Nice Ride 车站 2011 年出行数据。 Paul 博士介绍了三种普通最小二乘回归模型来评估企业存在对年度全站出行、出行出发地和出行目的地的边际效应。作者的模型包括四个一般类别的 19 个变量，除了不同类型的企业和工作的存在之外，还包括用作控制的社会人口、建筑环境和交通基础设施变量。结果显示，Nice Ride 车站的出行次数与车站附近的食品相关目的地和工作便利性呈正相关且显着相关，但与一般零售场所无关。自行车共享站的使用还与种族、年龄、与中央商务区的距离、与水源的距离、步道的可达性以及与其他自行车共享站的距离相关。这项研究对于规划者、学者和政策制定者来说非常重要，因为研究结果将有助于理解自行车共享运营，帮助规划者找到新的站点，评估实施新自行车共享计划的潜力，评估与自行车共享出行相关的经济活动，并最大限度地降低运营成本。
Bike sharing systems have been established in several cities across North America. An objective of all bike sharing programs is to maximize the number of trips to and from bike share stations. The purpose of this research is to identify correlates of bike station activity, with special emphases on the association of trips to and from bike stations with the number of nearby businesses and jobs. Using data on 2011 trips from Nice Ride stations in Minneapolis-St. Paul, the authors introduce three ordinary least square regression models to evaluate the marginal effects of the presence of businesses on annual total station trips, trip origins and trip destinations. The authors' models include 19 variables in four general categories, including, in addition to the presence of different types of businesses and jobs, sociodemographic, built environment, and transportation infrastructure variables that are used as controls. The result shows the number of trips at Nice Ride stations is positively and significantly associated with food-related destinations near the station and job accessibility but not with general retail establishments. Use of bike share stations also is correlated with race, age, proximity to the central business district, proximity to water, accessibility to trails, and distance to other bike share stations. This research is important for planners, academics, and policymakers because the findings will facilitate the understanding of bike share operations, help planners locate new stations, evaluate the potential of implementing new bike share programs, assess economic activity associated with bike share trips, and minimize costs of operations.