A Method for Measuring Bicycle-Sharing Tidal Flow Patterns from a Multi-level Perspective

To address the modifiable areal unit problem (MAUP) caused by traditional aggregation units such as grids in shared bike tidal pattern detection, as well as the difficulty in balancing detection accuracy and computational efficiency in large-scale road networks, this paper proposes a two-stage tidal pattern measurement method from a multi-level perspective of "management subzone-road segment granularity." In the first stage, road segments are used as the basic detection unit instead of grids to avoid MAUP interference caused by subjective aggregation boundary delineation. In the second stage, the Leiden community detection algorithm is introduced to divide the study area into several management subzones with strong internal connections, shrinking the detection range of road-segment-scale tidal patterns from the entire region to within each subzone and achieving an order-of-magni tude reduction in computational complexity. Taking the shared bike system on Xiamen Island as a case study, the tidal patterns during the period from 06:00 to 10:00 are detected and analyzed. The results show that: in terms of accuracy, compared with grid-based methods, the two-stage method achieves significantly better accuracy, with the proportions of "extremely high" and "relatively high" accuracy reaching 74% and 61%, respectively, while the grid-based methods achieve only approximately 20% and 12%; in terms of computational efficiency, the two-stage method improves efficiency by 66% compared with direct processing of the entire region. A total of 83% of the tidal patterns occur during the morning peak (07:00—09:00), concentrated near employment and residential areas, respectively, exhibiting a clear unidirectional tidal flow pattern. The detected tidal patterns exhibit arbitrary shapes and multi-scale distributions that are difficult to capture with regular grid units, and tidal intensity varies significantly across time periods, indicating the necessity of graded dispatch strategies. In conclusion, the proposed method effectively overcomes the MAUP effect while balancing detection efficiency and identification accuracy, and can effectively enhance operational feasibility.