共享单车流率的大小体现了城市空间环境内车辆盈缺的程度，理解其变化及其诱因对于城市单车的调度具有重要意义。由于出行目的和外界环境因素的复杂多变，共享单车流率和建成环境特征之间的关系很难通过具有线性假设的统计学模型来解析。基于此，本研究利用上海市中心城区的共享单车数据，基于极端梯度提升树模型（XGBoost）和机器学习的解释性方法部分依赖图（PDP）来探究建成环境对共享单车流率的贡献度和非线性影响，以及流率的非线性模式在工作日和周末的变化。结果显示，特征重要度和非线性机制在两个时段差异化显著。居住人口密度、教育设施密度和住宅设施密度对工作日单车流率的解释度较高，分别为19.18%、13.16%和12.92%，并且具有明显的阈值效应。其中居住人口密度和教育设施密度对于单车净流出率具有正向影响，分别在11 600 人/km²和8个/km²达到最大；住宅设施密度对单车净流出率具有负向影响，对应的阈值为40 个/km²。各变量对周末单车流率的解释度差异较小，但非线性关系仍不可忽视。具体来说，到市中心的距离和公交线数密度对周末单车净流入率正向影响显著，有效范围为18~23 km和28~52 条/km²；容积率对周末单车净流出率正向影响范围在0.89~1.41。上述发现表明XGBoost模型可以有效弥补传统回归模型（MLR）线性假设的偏见，建成环境特征贡献度和影响范围的揭示也为管理部门针对具有不同建成环境水平地区的单车调度提供决策建议。

The bike-sharing(BS) flow rate reflect the degree of surplus and shortage of vehicles in urban spatial environment. Understanding its changes and incentives is of great significance for urban BS scheduling. Due to the complexity and variability of travel purposes and external environmental factors, it is difficult to analyze the relationship between the BS flow rate and the characteristics of the built environment through a statistical model with linear assumptions. Therefore, this study explored the contribution of the built environment to the BS flow rate and the nonlinear effects on the flow rate, as well as the changes of the nonlinear model of the BS flow rate on weekdays and weekends based on the data of BS in the downtown of Shanghai, through the extreme gradient boosting tree model (XGBoost) and the interpretive method partial dependence plot (PDP) of machine learning. The results show that the feature importance and nonlinear mechanism are significantly different in the two periods. The density of residential population, educational facilities and residential facilities has a high degree of explanation for the weekday BS flow rate, which is 19.18%, 13.16% and 12.92%, respectively, and has a significant threshold effect. The density of residential population and the density of educational facilities have a positive impact on the net BS outflow rate, reaching the maximum at 11 600 person per km² and 8 educational facilities per km² respectively; the density of residential facilities has a negative impact on the net BS outflow rate, and the corresponding threshold is 40 residential facilities per km².There is little difference in the explanatory degree of each variable to weekend BS flow rate, nevertheless the nonlinear relationship cannot be ignored. Specifically, the distance to the city center and bus line number density have a significant positive impact on the weekend net BS inflow rate, with the effective range of 18~23 km and 28~52 routes per km². The positive influence range of plot ratio on net BS outflow rate at weekends is 0.89~1.41. The above findings show that XGBoost model can effectively compensate for the bias of linear assumption of traditional regression model (MLR), and the disclosure of the contribution degree and influence scope of built environment characteristics also provides decision-making suggestions for the management department for BS dispatching in areas with different built environment levels.