AI 中文总结
该研究针对共享单车系统引力模型参数的时空变化问题，通过滑动窗口和分区域的空间建模分析8个系统，发现了早晚高峰及不同半径下的依赖关系差异，提出需结合多时段多流量类型进行全天城市范围拟合。
AI 中文摘要
引力模型将共享单车的起讫点（OD）流量描述为随起点和终点活动量增加而增加、随距离增加而减少的函数，但这些关系在一天内及不同观测区域内如何变化仍不明确。我们研究了8个共享单车系统中起点指数α、终点指数β、距离指数γ，以及R²和平均绝对误差（MAE）的时空变化。时间建模采用滑动窗口，空间建模则扩展圆形区域并划分为区内出行、区际流出、区际流入和跨区出行。我们发现各城市存在重复模式：早高峰和晚高峰的起点与终点依赖关系不同，随半径增大，同一边界的区际流出和区际流入在相对起点与终点依赖关系上呈相反变化。模型性能和距离依赖关系也随时间和半径变化。因此，覆盖全天的城市范围拟合需结合具有不同引力关系的时段和流量类型。
英文摘要
Gravity models describe bike-sharing origin-destination (OD) flows as increasing with origin and destination activity and decreasing with distance. Yet how these relationships change within a day and across observation areas remains unclear. We examine temporal and spatial variation in origin, destination and distance exponents $α$, $β$, and $γ$, together with $R^2$ and mean absolute error (MAE), across eight bike-sharing systems. Temporal modeling uses sliding windows, while spatial modeling expands a circular area and separates intra-zonal, cross-zonal outflow, cross-zonal inflow and extra-zonal trips. We find recurring patterns across cities: morning and evening peaks differ in origin and destination dependence, while cross-zonal outflow and inflow across the same boundary show opposite changes in their relative origin and destination dependence as radius increases. Model performance and distance dependence also vary with time and radius. A full-day, citywide fit therefore combines time periods and flow types with different gravity relationships.
CommentsSupplementary information is provided as an ancillary file
