Shared bicycle provides a cheap and healthy mobility alternative to travelers especially for the “first–last mile” trips. Although the temporal and spatial correlation of regional shared bicycle needs has been confirmed in the literature in recent years, the interdependencies between them are not yet fully understood. In this paper, a spatio-temporal Bayesian modeling method is proposed to quantify regional shared bicycle demand and identify the impact of various factors on the cycling trips. By combining the Integrated Nested Laplace Approximation (INLA) and Stochastic Partial Differential Equation (SPDE), it guarantees the establishment of the feasibility of algorithms on large-scale spatiotemporal data structures. In particular, the massive rental records of Mobike in Shanghai in August 2016 are used as the study observation. We establish a series of Bayesian models with different temporal and spatial structures, and uses the Deviation Information Criteria to verify the relevance of the models in the temporal and spatial dimensions. Moreover, the Kolmogorov–Smirnov test is proposed to fit different distributions to obtain the optimal distribution family of travel demand data. Our research efforts have further been made to evaluate the impact of meteorological factors, population density and per capita GDP on travel demand. The result shows that the model of temporal and spatial correlation structure can better assess the regional distribution of future shared bicycle riding demands, and the influence of temperature and precipitation on cycling demand is more significant. The study’s findings will help guide the development of future shared bicycle regional scheduling work, and improve economic benefits on the basis of meeting traveler’ needs.

共享单车为旅行者提供了一种廉价且健康的出行方式，特别是对于“第一至最后一英里”的旅行。近年来，虽然区域共享单车需求的时空相关性已经在文献中得到证实，但它们之间的相互依存关系尚未得到充分理解。在本文中，提出了一种时空贝叶斯建模方法来量化区域共享单车需求并识别各种因素对自行车出行的影响。通过结合集成嵌套拉普拉斯近似（INLA）和随机偏微分方程（SPDE），保证了算法在大规模时空数据结构上的可行性。尤其以摩拜单车2016年8月在上海的海量租赁记录作为研究观察。我们建立了一系列具有不同时空结构的贝叶斯模型，并使用偏差信息准则来验证模型在时空维度上的相关性。此外，提出了 Kolmogorov-Smirnov 检验来拟合不同的分布，以获得出行需求数据的最优分布族。我们进一步开展了研究，以评估气象因素、人口密度和人均 GDP 对出行需求的影响。结果表明，时空关联结构模型可以更好地评估未来共享单车骑行需求的区域分布，且气温和降水对骑行需求的影响更为显着。