Mobility-as-a-service systems are becoming increasingly important in the context of smart cities, with challenges arising for public agencies to obtain data from private operators. Only limited mobility data are typically provided to city agencies, which are not enough to support their decision-making. This study proposed an entropy-maximizing gravity model to predict origin–destination patterns of both passenger and mobility fleets with only partial operator data. An iterative balancing algorithm was proposed to efficiently reach the entropy maximization state. With different trip length distributions data available, two calibration applications were discussed and validated with a small-scale numerical example. Tests were also conducted to verify the applicability of the proposed model and algorithm to large-scale real data from Chicago transportation network companies. Both shared-ride and single-ride trips were forecast based on the calibrated model, and the prediction of single-ride has a higher level of accuracy. The proposed solution and calibration algorithms are also efficient to handle large scenarios. Additional analyses were conducted for north and south sub-areas of Chicago and revealed different travel patterns in these two sub-areas.

在智慧城市的背景下，移动即服务系统变得越来越重要，公共机构从私人运营商那里获取数据面临着挑战。通常仅向城市机构提供有限的移动性数据，这不足以支持其决策。这项研究提出了一个最大熵熵模型，仅使用部分操作员数据即可预测客运和机动车队的始发地-目的地模式。提出了一种迭代均衡算法来有效地达到熵的最大化状态。利用不同的行程长度分布数据，讨论了两个校准应用程序，并使用一个小规模的数值示例进行了验证。还进行了测试，以验证所提出的模型和算法对来自芝加哥交通网络公司的大规模真实数据的适用性。基于标定模型对共享出行和单程出行进行了预测，单程出行的预测具有较高的准确性。所提出的解决方案和校准算法对于处理大型场景也很有效。对芝加哥的北部和南部子区域进行了进一步的分析，揭示了这两个子区域中不同的出行方式。