The accessibility of commercial centers (CCs) is a topic of great concern to consumers and planners. However, current research is mostly limited to the accessibility of a single traffic mode or rough road network, which makes it difficult to reflect on the real travel situation of residents. Based on the two-step floating catchment area (2SFCA) method framework, we propose an extended method for analyzing the accessibility of CCs in different transportation modes, making it suitable for commercial geography research. The Baidu Map API is used to provide more accurate, convenient, and realistic data for reliable estimates to pinpoint underserved populations. The results can be summarized as follows: First, the multimode R2SFCA method proposed fills the knowledge gap to the accessibility of a single traffic mode or or rough road network. Second, there exist significant differences in the spatial distribution of access to CCs for consumers in different transportation modes. The range of public transit along metro or bus routes shows a finger-like spatial distribution, while car traffic extends along external urban traffic routes and is time-sensitive because of traffic conditions. Third, changes in the average accessibility scores for all travel modes showed similar patterns with increasing threshold time. These accessibility measures help identify underserved areas and enable the development of policy recommendations for the optimal allocation of commercial facilities and resources.