Multi-objective optimization of urban logistics land: a gradient-based method approach with Wuhan city as an example

Effective planning of logistics land is crucial for mitigating urban freight congestion, fostering economic activities, and achieving environmental equilibrium. However, the dual challenge of mismatched logistics supply and demand, along with conflicts in land use functions, can lead to inefficiencies in resource allocation and urban freight system performance. To tackle this issue, our study integrates truck GPS trajectory data with urban land use datasets to formulate a multi-objective optimization model. By utilizing the gradient descent algorithm, which effectively handles large-scale datasets, we can navigate the complexities of logistics land planning with precision. The application of this model in the Wuhan Urban Development Area reveals that: (1) across various scenarios, the model balances the utilization of multiple optimization objectives and demonstrates high solution efficiency; (2) in both the equal weight scenario and the economic preference scenario, the areas of logistics land change are characterized by high economic output, relatively good traffic conditions, greater distance from residential zones, and comparatively low land prices; and (3) based on urban development goals, the model can determine the upper bounds of the optimization objectives through manual supervision of the selection of ideal points. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.