分析上海共享单车自循环现象的空间异质性，揭示用地与人口特征的影响。
Multiscale spatiotemporal heterogeneity analysis of bike-sharing system's self-loop phenomenon: Evidence from Shanghai
- 采用空间自回归与双重机器学习框架，分地铁站与街道尺度分析影响因素。
- 街道尺度自循环强度受住宅用地正向影响，中龄居民区效应更显著。
- 高地铁使用、低公交覆盖区域应增加自行车供给并动态调配。
共享单车是一种环保的共享出行方式，但其自循环现象（即车辆被多次使用后返回原站点）严重影响服务公平性。本研究基于空间自回归模型与双重机器学习框架，对上海地铁站点与街道尺度的自循环现象进行多尺度分析，探讨社会经济特征与地理空间位置的影响。结果表明，街道尺度的自循环强度存在显著空间滞后效应，且与住宅用地呈正相关。住宅用地的边际处理效应在中龄居民、固定就业率高、私家车拥有率低的街道更高。多模式公共交通条件在两个尺度均呈现显著正向边际效应。为提升共享单车协同效率，建议在地铁使用率高但公交覆盖不足的区域增加自行车投放，并实施灵活的调度策略。
原文摘要 · Abstract (English)
Bike-sharing is an environmentally friendly shared mobility mode, but its self-loop phenomenon, where bikes are returned to the same station after several time usage, significantly impacts equity in accessing its services. Therefore, this study conducts a multiscale analysis with a spatial autoregressive model and double machine learning framework to assess socioeconomic features and geospatial location's impact on the self-loop phenomenon at metro stations and street scales. The results reveal that bike-sharing self-loop intensity exhibits significant spatial lag effect at street scale and is positively associated with residential land use. Marginal treatment effects of residential land use is higher on streets with middle-aged residents, high fixed employment, and low car ownership. The multimodal public transit condition reveals significant positive marginal treatment effects at both scales. To enhance bike-sharing cooperation, we advocate augmenting bicycle availability in areas with high metro usage and low bus coverage, alongside implementing adaptable redistribution strategies.
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