Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan

Abstract

Urban logistics is important to a city’s sustainable growth and development. With the increase in population and the economic growth in urban areas, the issue of congestion and the negative influence of transport of goods on people and the environment is one of the most important factors in the development of urban logistics. By determining the optimal location of urban logistics centres, total transport costs of logistics, the flow of goods in urban areas and the greenhouse gas emissions will be reduced. However, the traditional methods are easily influenced by the ambiguity of objective data, which makes it difficult to accurately describe the logistics demand in the urban area. To address this issue, the improved location–allocation model for urban logistics centres based on truck trajectory data is proposed. After extracting the origin–destination points, the logistics service demand can be estimated by the DBSCAN (density-based spatial clustering of applications with noise) clustering method. Then, the location–allocation of logistics centres is determined by an improved P-median method with the supply capacity limitation for simultaneous delivery of goods in the logistics centres. To validate the model, taking Wuhan, a central logistics city in China, as an example, the model can effectively ensure the equitable distribution of logistics facilities, minimize freight expenses, achieve more uniformity in logistics center services, and foster sustainable development of the city’s logistics sector.