Predicting citizens’ visiting behaviors to urban facilities is instrumental for city governors and planners to detect inequalities in urban opportunities and optimize the distribution of facilities and resources. Previous works predict facility visits simply using observed visit behavior, yet citizens’ intrinsic demands for facilities are not characterized explicitly, causing potential incorrect learned relations in the prediction results. In this article, to make up for this deficiency, we present a demand-driven urban facility visit prediction method that decomposes citizens’ visits to facilities into their unobservable demands and their capability to fulfill them. Demands are expressed as the function of regional demographic attributes by a neural network, and the fulfillment capability is determined by the urban region’s spatial accessibility to facilities. Extensive evaluations of datasets of three large cities confirm the efficiency and rationality of our model. Our method outperforms the best state-of-the-art model by 8.28% on average in facility visit prediction tasks. Further analyses demonstrate the reasonableness of recovered facility demands and their relationship with citizen demographics. For instance, senior citizens tend to have higher medical demands but lower shopping demands. Meanwhile, estimated capabilities and accessibilities provide deeper insights into the decaying accessibility with respect to spatial distance and facilities’ diverse functions in the urban environment. Our findings shed light on demand-driven urban data mining and demand-based urban facility planning.

预测公民对城市设施的访问行为有助于城市管理者和规划者发现城市机会的不平等并优化设施和资源的分配。先前的工作仅使用观察到的访问行为来预测设施访问，但公民对设施的内在需求并未明确表征，导致预测结果中可能存在错误的学习关系。在本文中，为了弥补这一缺陷，我们提出了一种需求驱动的城市设施访问预测方法，该方法将公民对设施的访问分解为不可观测的需求和满足这些需求的能力。需求通过神经网络表达为区域人口属性的函数，而满足能力则由城市区域设施的空间可达性决定。对三个大城市数据集的广泛评估证实了我们模型的效率和合理性。在设施访问预测任务中，我们的方法平均比最先进的模型高出 8.28%。进一步的分析证明了恢复设施需求的合理性及其与公民人口统计的关系。例如，老年人的医疗需求往往较高，但购物需求较低。同时，估计的能力和可达性可以更深入地了解城市环境中空间距离和设施多样化功能方面的可达性衰减。我们的研究结果揭示了需求驱动的城市数据挖掘和基于需求的城市设施规划。