Typical Routes and Sequential Movement Patterns in Commercial Spaces

Commercial spaces are essential urban environments where consumer movement is inherently sequential, involving multiple destinations and complex spatial interactions. However, existing studies mainly rely on aggregated flow data and provide a limited understanding of how consumers organize their movements into complete spatial sequences. To address this limitation, this study proposes a framework based on typical routes to investigate sequential movement patterns in commercial spaces. Typical routes are conceptualized in two complementary ways: as representative routes extracted from observed traces and as highly probable routes generated from sequential destination choices. Accordingly, two approaches are integrated. First, route clustering is conducted using Levenshtein-ratio-based similarity measurement and affinity propagation (AP) clustering to identify representative route exemplars with distinct sequential structures. Second, route prediction is performed by combining discrete choice models (DCM), convolutional neural networks (CNN), and beam search to generate high-probability movement sequences. The framework is applied to 323 observed consumer routes collected from a large commercial complex in Shanghai, China. The results reveal several recurring movement patterns, such as task-oriented, fashion-oriented, dining-oriented, and family-oriented routes. Among them, the most dominant pattern is characterized by repeated movement between anchor stores through sequences of intermediate, smaller shops, highlighting the organizing role of anchor destinations in structuring consumer circulation. Both clustering and prediction demonstrate that consumer movement in commercial spaces is not random, but follows relatively stable sequential logics. In the prediction task, CNN achieves substantially higher accuracy than DCM, thereby providing stronger support for route simulation. Overall, the study demonstrates that integrating route clustering and route prediction provides a useful framework for understanding, interpreting, and simulating sequential consumer movement in commercial environments, with practical implications for circulation organization, anchor-store layout, and commercial space design.