针对新建公共自行车站点无法根据历史数据对未来使用需求进行预测的问题，提出了一种修正的地理加权回归模型探索单位时间节点需求生成与可达性间的关系。改进的模型以路网距离作为限制条件，获取基于泰森多边形的站点吸引重叠区域。同时，为减小用地区位导致的预测误差，改进的模型增添用地混合度和建筑强度作为解释变量。利用所提出的模型对西安市公共自行车系统运营数据分析的结果表明，各类用地最大需求生成率集中在早晚高峰时段，并在以天为周期的循环中具有各异的变化规律。公共自行车系统的需求生成率随着起点/终点与目标停靠站点之间距离增大而逐渐减小，表现为早高峰时期的线性衰减，晚高峰时期的指数衰减以及非高峰时期的立方衰减。研究成果可用于确定西安市新建公共自行车站点位置和规模，并预测其相应的使用需求率。

Aiming at the problem that newly built public bicycle stations cannot predict future use demand based on historical data, a modified geographically weighted regression model was proposed to explore the relationship between demand generation and accessibility at unit time nodes. The modified model took road network distance as the constraints to access the overlapping attraction area of docking stations based on Thiessen Polygon. Meantime, in order to reduce the prediction error caused by land location, the modified model added land mix degree and building strength as explanatory variables.The proposed model was utilized to analyze data from the docked bike-sharing system in Xian. The results indicate that the production rates of various land types are found to be maximum in the morning and evening peak hours with variable patterns in daily change. The production rate of bike-sharing system will gradually decrease as the distance between origin/destination and target docking stations increases, showing linear attenuation in the morning peak period, exponential attenuation in the evening peak period and cubic attenuation in the non-peak period. The findings can be utilized to determine the location and scale of new docking stations of bike-sharing system in Xian and predict relative usage demand rates.