Urban Mobility Models (UMMs) are fundamental tools for estimating the population in urban sites and their spatial movements over time. Most existing UMMs were developed primarily in 2D. However, we argue that people’s movements and living patterns involve 3D space, i.e., buildings, which can heavily affect the accuracy of UMMs. In this article, we for the first time conduct a comprehensive study on the impacts of buildings on human movements and the effect on UMMs. We innovatively capture the impacts by developing a Semi-absorbing Urban Mobility model (SUM) and theoretically prove its properties on its difference from that of previous UMMs. We also show that calibrating our original SUM may need a large number of parameters. As such, we develop two SUM extensions with a substantially reduced number of parameters, making calibration practical. Our evaluation also demonstrates that, as a basis for supporting mobile applications in an intracity and hourly scale, the SUM is far superior to previous UMMs. In a case study, we also show that the performance of the resource allocation scheme in a cellular network substantially improves by using SUM, with a reduction in the packet loss probability of 3.19 times.

中文翻译：

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涉及建筑物的城市交通模型

城市流动模型 (UMM) 是估计城市地点人口及其随时间的空间移动的基本工具。大多数现有的 UMM 主要是在 2D 中开发的。然而，我们认为人们的运动和生活模式涉及 3D 空间，即建筑物，这会严重影响 UMM 的准确性。在本文中，我们首次对建筑物对人类运动的影响以及对 UMM 的影响进行了全面研究。我们通过开发半吸收式城市交通模型 (SUM) 创新地捕捉影响，并从理论上证明其与之前 UMM 不同的特性。我们还表明，校准我们的原始 SUM 可能需要大量参数。因此，我们开发了两个 SUM 扩展，其参数数量大大减少，使校准变得实用。我们的评估还表明，作为支持城市内和每小时规模的移动应用程序的基础，SUM 远远优于以前的 UMM。在一个案例研究中，我们还表明，蜂窝网络中资源分配方案的性能通过使用 SUM 显着提高，丢包概率降低了 3.19 倍。