Unraveling Bus Passenger OD Flows Through the Lens of Industrial Spatial Structure with Proximity and Scarcity Metrics: A Case Study of Beijing, China

The urban industrial spatial structure is widely recognized as a critical factor shaping public transit travel patterns. However, the relationship between variations in this spatial structure and bus origin-destination (OD) passenger flows remains insufficiently explored. To address this gap, this study proposes two quantitative indicators, namely, Regional Industrial Proximity (RIP) and Regional Industrial Scarcity (RIS), to characterize the differentiation of urban industrial spatial structures. The empirical study is conducted in the core built-up area within Beijing’s 5th Ring Road. The study area is divided into 8.3 km × 8.3 km grids, and a Proximity–Scarcity Relational Graph Convolutional Network (PS-RGCN) model is employed to predict urban bus passenger flows. The dataset primarily comprises approximately 4.21 million smart card transaction records collected over one continuous week, alongside 276,000 Points of Interest (POI) records covering 20 industrial categories. Specifically, the proposed PS-RGCN model achieves the best prediction performance among all benchmark models. The best overall performance is attained when incorporating the RIP of Scenic Spots and Historical Sites as edge relationships, yielding a coefficient of determination R2 of 0.888. In comparison, the gravity model yields an R2 of 0.858, and the baseline GCN model yields an R2 of 0.378, indicating the superior predictive capability of the proposed model. This study verifies that industrial proximity and industrial scarcity exert complementary effects in bus OD flow prediction. Incorporating both factors synergistically into the multi-relational graph neural network framework enables more effective identification of the predictive relationship between urban functions and bus travel demand.