Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we derive a censored likelihood function capable of handling time-varying supply. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.

运输需求高度依赖供应，特别是对于可用性通常有限的共享运输服务。由于观察到的需求不能高于可用供应，因此历史运输数据通常代表真实基础需求模式的有偏见或经过审查的版本。如果没有明确说明这种固有的区别，需求的预测模型就必然代表真实需求的有偏差版本，从而不太有效地预测服务用户的需求。为了解决这个问题，我们提出了一种用于审查意识的需求建模的通用方法，为此我们推导了一个被审查的方法。能够处理时变供应的似然函数。通过将经过审查的似然性纳入高斯过程模型中，我们可以将该方法应用于共享出行需求预测的任务，该模型可以灵活地近似任意函数形式。在人工和现实数据集上进行的实验表明，在获得用户需求行为的无偏预测模型的过程中，如何考虑供需的局限性至关重要。