基于空间滞后模型的公共自行车出行特征及影响因素分析

为充分挖掘公共自行车时空出行特征，探讨城市空间环境与骑行需求的潜在联系。以宁波市中心城区为案例，基于公共自行车IC卡数据获取出行时空变化规律，在验证租、还车需求具有空间自相关性的基础上，通过建立空间滞后模型，分析了人口密度、道路分布、公共交通、站点配置和建成环境因素对骑行需求的影响。研究表明:①工作日、非工作日内租、还车需求的全局Moran's I分别为0.294，0.281，0.272和0.271，表现出显著的空间正相关性；②各模型的拟合优度R2分别是0.431，0.424，0.412，0.401，具有良好的拟合效果与解释性；③道路分布、建成环境变量对公共自行车使用的影响效应存在时间差异，其中公交专用道里程与非工作日内的站点需求量呈负相关，工作日内POI混合度对租、还车需求具有正向引导作用。 

Travel Characteristics and Influencing Factors of Bike Sharing Based on Spatial Lag Model

The paper aims to investigate the spatial-temporal characteristics of the bike sharing system(BSS)and quantify factors affecting BSS usage from the urban spatial environment. The spatio-temporal analysis is conducted to investigate the mobility pattern of BSS using the massive IC-card data in central urban area of Ningbo, China. By considering the spatial autocorrelation of pick-up and drop-off, a spatial lag model is established to identify the internal relationship between BSS usage and spatial variables from population density, road distribution, public transportation, station infrastructure, and built environment. The results show that: ①The global Moran's I for pick-up and drop-off on weekdays and weekends is 0.294, 0.281, 0.272, and 0.271, indicating the spatial correlation is significantly positive. ②The goodness of fit is 0.431, 0.424, 0.412, and 0.401, showing that these models have good fitness and explanatory. ③There are also significant temporal differences between road distribution and built environment variables influencing BSS usage. The length of bus lanes is negatively correlated with the usage demand during weekends, and the POI mixing degree positively affects the demand for pick-up and drop-off on weekdays.