Employment center locations, the jobs-housing relationship, and commuting patterns are inextricably connected in a megacity that heavily relies on the urban transit system to shuttle commuters. Using a transit smartcard dataset, a machine learning method is employed as a preliminary filter to sift through commuter flows and isolate key data, then kernel density and topological analysis methods are implemented to delve into the primary research questions, providing a detailed look at how these patterns unfold. The results show that transit commuting behavior in Shanghai is a dispersal activity based on multi-level employment centers characterized as hierarchical, boundary-clear, and functionally oriented. Further analysis illustrates that the intensity of commuting linkages correlates with the employment center level, shaping a hybrid pattern that couples a core-periphery pattern with a spoke-hub pattern. The commuting network connections present corridor, neighborhood, capture, and replacement features, highlighting the importance of employment centers in shaping commuting patterns. From a daily flow perspective, these findings echo the central place theory and verify that the employment center distribution in Shanghai is also multi-layered and well-nested, forming the basis for commuting links. Policy implications are provided for polycentric megacities with progressively sophisticated urban transit systems.
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