Replication Data for: A National Study of Dockless Transportation: Land Use and Demographic Correlates of Trip Hotspots and Mode Shift

This research builds a land use regression model to explain dockless scooter trip generations. We use publicly available scooter trip generation data for Louisville, KY and Minneapolis, MN and publicly available data on land use characteristics. The model shows that scooter trip generations are associated with higher employment densities, higher densities of entertainment land uses (bars and clubs), and in some specifications higher densities of eating establishments and university buildings. We establish that using the regression results to predict out of sample gives predictions that correspond well to observed scooter trip generations in Austin, TX. Because scooter trip data are not available for research in California, we use the Minneapolis model to predict scooter trips as a function of land use characteristics in California census tracts. The results yield a promising screening method that can highlight census tracts with land use characteristics that are potentially supportive of micro-mobility and non-automobile short-trip travel. We recommend that such a screening method can be a first step in more detailed analyses of planning programs or infrastructure that could support non-automobile short-trip travel.本研究构建土地使用回归模型，以阐释无桩共享滑板车的出行生成规律。本研究采用公开可得的肯塔基州路易斯维尔市与明尼苏达州明尼阿波利斯市的滑板车出行生成数据，以及土地使用特征相关的公开数据集。模型结果显示，滑板车出行生成量与更高的就业密度、娱乐用地（酒吧与俱乐部）密度显著正相关；在部分模型设定下，其还与餐饮场所及高校建筑的更高密度相关。本研究验证了：利用该回归模型结果开展样本外预测时，所得预测结果与德克萨斯州奥斯汀市实测的滑板车出行生成量吻合度较高。由于加州暂无可供科研使用的滑板车出行数据，我们借助明尼阿波利斯市的模型，基于加州人口普查区的土地使用特征预测滑板车出行量。研究结果得到一种颇具应用前景的筛选方法，可识别出土地使用特征潜在适配微出行与非机动短途出行的人口普查区。我们建议，该筛选方法可作为开展更细致分析的第一步，用于评估可支持非机动短途出行的规划方案或基础设施建设。