The implementation of the urban underground logistics system (ULS) can effectively mitigate the contradiction between the surging logistics demand and the increased negativity of urban logistics. The widespread implementation of ULS still suffers from a lack of research into its operation in the marketplace, although the research on ULS system technology and network design appears to be sufficient. A new supply chain for logistics service based on ULS (ULS-SSC) was proposed, as ULS embedded in the urban logistics system could lead to the evolution of the role of supply chain participants. This article analyzed the organizational structure and operation characteristics of ULS-SSC and designed a top-down ULS-SSC operation process model based on the designed functional structure and subsystems relationship using the hierarchical colored Petri net (HCPN). The simulation results show that the integrated information management platform based on ULS can integrate urban logistics service supply chain resources and operate effectively under the two main service modes designed. The high-time delay intermediate links can be upgraded by system optimization, and the links with initial pickup and terminal distribution can be improved through outsourcing and supply chain collaboration. The findings provide new insights into the feasibility of the operation of ULS in the market and help stimulate the implementation of ULS.

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.