Rapid urbanization has dramatically changed the urban spatial structures, causing a mismatch between residents' commuting activities and the optimal status of the current urban facility configuration. However, limited attention has been paid to detecting these mismatched commuting patterns and their associations with built environmental characteristics. To maximize the effectiveness of urban facility allocation and improve commuting efficiency, this paper developed a framework to identify anomalous commuting interaction patterns. A weighted bipartite network considering urban land attractiveness was first constructed to analyze the commuting flows between urban units. Then a modified Hungarian algorithm was proposed to obtain the optimal commuting interaction fluxes. By comparing real and optimal interaction fluxes, two types of commuting anomalies were detected. Finally, the machine learning model was used to explore the non-linear relationships between built environment and anomalous commuting patterns. Results show the spatial distribution of areas with significant anomalous interactions and the difference between overload- and underload- related anomalous commuting patterns. Potential urban sub-centers were identified to adjust the urban spatial layouts. Besides, the nonlinear and threshold effects of the built environment on the two anomalous commuting patterns were confirmed, which can provide references for urban spatial renewal and commuting flow allocation.

中文翻译：

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检测异常通勤模式：城市土地吸引力与通勤活动之间的不匹配

快速城市化极大地改变了城市空间结构，导致居民通勤活动与现有城市设施配置的最优状态不匹配。然而，人们对检测这些不匹配的通勤模式及其与建筑环境特征的关联的关注有限。为了最大限度地发挥城市设施配置的有效性并提高通勤效率，本文开发了一个框架来识别异常通勤交互模式。首先构建考虑城市土地吸引力的加权二分网络来分析城市单元之间的通勤流量。然后提出了一种改进的匈牙利算法来获得最优的通勤交互通量。通过比较真实和最佳交互通量，检测到两种类型的通勤异常。最后，利用机器学习模型探索建筑环境与异常通勤模式之间的非线性关系。结果显示了具有显着异常相互作用的区域的空间分布以及与过载和欠载相关的异常通勤模式之间的差异。识别潜在城市副中心，调整城市空间布局。此外，还证实了建成环境对两种异常通勤模式的非线性和阈值效应，可为城市空间更新和通勤流量分配提供参考。