Bike sharing is an increasingly popular mobility choice as it is a sustainable, healthy and economically viable transportation mode. By interpreting rides between bike stations over time as temporal events connecting two bike stations, relational event models can provide important insights into this phenomenon. The focus of relational event models, as a typical event history model, is normally on dyadic or node-specific covariates, as global covariates are considered nuisance parameters in a partial likelihood approach. As full likelihood approaches are infeasible given the sheer size of the relational process, we propose an innovative sampling approach of temporally shifted non-events to recover important global drivers of the relational process. The method combines nested case-control sampling on a time-shifted version of the event process. This leads to a partial likelihood of the relational event process that is identical to that of a degenerate logistic additive model, enabling efficient estimation of both global and non-global covariate effects. The computational effectiveness of the method is demonstrated through a simulation study. The analysis of around 350,000 bike rides in the Washington D.C. area reveals significant influences of weather and time of day on bike sharing dynamics, besides a number of traditional node-specific and dyadic covariates.

翻译：自行车共享作为一种可持续、健康且经济可行的交通方式，正日益成为流行的出行选择。通过将自行车站点间随时间发生的骑行活动解释为连接两个站点的时序事件，关系事件模型能够为这一现象提供重要洞见。作为典型的事件史模型，关系事件模型通常关注二元或节点特定协变量，因为在偏似然方法中全局协变量被视为冗余参数。鉴于关系过程的庞大规模，全似然方法并不可行。为此，我们提出了一种创新的时间偏移非事件抽样方法，以恢复关系过程的重要全局驱动因素。该方法在事件过程的时间偏移版本上采用嵌套病例对照抽样，从而得到与退化逻辑可加模型完全一致的关系事件过程偏似然，实现了对全局与非全局协变量效应的高效估计。通过模拟研究验证了该方法的计算效能。对华盛顿特区约35万次骑行数据的分析表明，除一系列传统的节点特定协变量与二元协变量外，天气条件和时段因素对自行车共享动态同样具有显著影响。