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In practice, the ride-hailing platform generally provides two products: express services and ridesplitting services, and driver resources are usually shared across these two products on the supply side, i.e., hybrid ride-hailing mode. This study tackles the order dispatch problem with considering flexible one-to-three matching strategy under hybrid modes. In this problem, if a driver serves express requests, only one order can be matched with; if a driver serves ridesplitting requests, this driver can be matched with at most three orders simultaneously or sequentially. Considering one-to-three matching, en-route matching, and multiple passengers per order, this problem is formulated as a multi-objective integer programming, a hierarchical approach is adopted to address it. Using the ride-hailing trip data, we examine characteristics of this problem, such as the impact of service priority and the benefits of one-to-three matching. Finally, some beneficial management implications are proposed to improve the operation of the ride-hailing service. [ABSTRACT FROM AUTHOR]

Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi‐view building clustering method that captures cross‐view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple‐attribute graph). The three graphs undergo iterative cross‐diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real‐world community‐building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering. [ABSTRACT FROM AUTHOR]