Zheng Yixuan Y, Shida Yohei, Takayasu Hideki, Takayasu Misako
Department of Systems and Control Engineering, School of Engineering, Institute of Science Tokyo, Yokohama, Kanagawa, Japan.
Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan.
PLoS One. 2025 Aug 12;20(8):e0329603. doi: 10.1371/journal.pone.0329603. eCollection 2025.
Metropolitan commuting flows reveal crucial insights into urban spatial dynamics; however, existing mobility models often struggle to capture the complex, heterogeneous patterns within these regions. This study introduces the Spatially Segregated Urban Gravity (SSUG) model, a novel approach that synergistically combines urban classification with gravity-based flow prediction to address this limitation. The SSUG model's key innovations include: (1) demonstrating the existence of different scaling laws in metropolitan areas, (2) identifying the existence of data-driven bifurcation that delineates urban-suburban commuting behaviors, (3) employing scaling exponents to reveal spatial segregation, and (4) leveraging high-resolution Global Positioning System (GPS) data for precise deterrence factor measurement. This multifaceted approach enables simultaneous improvement in flow prediction accuracy and robust urban functional classification. Empirical validation across six diverse Japanese metropolitan areas-Tokyo, Osaka, Nagoya, Fukuoka, Sendai, and Sapporo-demonstrates the SSUG model's superior predictive power compared to traditional gravity models. Our results unveil previously undetected patterns of spatial structure and functional segregation, particularly highlighting the divergent commuting dynamics between urban cores and suburban peripheries. The SSUG model's capacity to discern fine-grained urban-suburban differences while accurately forecasting commuting flows offers transformative potential for evidence-based urban planning. By providing a more nuanced understanding of metropolitan mobility patterns, this study equips policymakers with a powerful tool for optimizing transportation networks, refining land-use strategies, and fostering sustainable urban development in increasingly complex metropolitan landscapes.
大都市通勤流揭示了城市空间动态的关键见解；然而，现有的交通流模型往往难以捕捉这些区域内复杂、多样的模式。本研究引入了空间隔离城市引力（SSUG）模型，这是一种将城市分类与基于引力的流量预测协同结合的新方法，以解决这一局限性。SSUG模型的关键创新包括：（1）证明大都市地区存在不同的标度律，（2）识别划分城市 - 郊区通勤行为的数据驱动分岔的存在，（3）使用标度指数揭示空间隔离，以及（4）利用高分辨率全球定位系统（GPS）数据进行精确的威慑因子测量。这种多方面的方法能够同时提高流量预测准确性和进行稳健的城市功能分类。在日本六个不同的大都市地区——东京、大阪、名古屋、福冈、仙台和札幌——进行的实证验证表明，与传统引力模型相比，SSUG模型具有更强的预测能力。我们的结果揭示了以前未被发现的空间结构和功能隔离模式，特别突出了城市核心区和郊区边缘之间不同的通勤动态。SSUG模型在准确预测通勤流量的同时辨别城市 - 郊区细微差异的能力为基于证据的城市规划提供了变革潜力。通过提供对大都市交通流模式更细致入微的理解，本研究为政策制定者提供了一个强大的工具，用于优化交通网络、完善土地利用策略，并在日益复杂的大都市景观中促进可持续城市发展。
