Road traffic condition constantly changes in both spatial and temporal domains. Traffic information of high spatiotemporal resolution is valuable to the study of road traffic dynamics. However, the massive data volume of high resolution traffic information of large spatiotemporal extent introduces difficulties in data organization and management. Actually, there is no well-accepted spatiotemporal data model specialized for management of such data that is effective in terms of data storage and access efficiencies. To overcome such drawbacks, this paper proposes a base state amendment model (BSAM) for dynamic traffic condition, which is based on linear referencing system. The model utilizes the basic strategy of BSAM to do lossless data compression in the time dimension while compresses data in the spatial domain by utilizing linear referencing system (LRS) and dynamic segmentation. In addition, road stroke instead of road segment is used for route identification in the LRS to further reduce data volume. We validate the effectiveness of the proposed model through its use in traffic condition data modeling in Chengdu urban area. Six variants of the proposed model and their characteristics are analyzed and compared. The six variants of the proposed model are denoted as BSAMa, BSAMb, BSAMc, BSAMd, BSAMe and BSAMf, respectively (see section 3). The main differences between them are in the number of base states, time interval between two base states and the number of non-base states. Data storage and accessing efficiencies of the six variants are qualitatively and quantitatively analyzed. The results indicate that BSAMf (see Fig. 4) has the optimal storage and accessing efficiencies for the data used. Furthermore, a method is suggested to estimate the number of non-base states between successive base states in the BSAMf model, which is based on the dynamic characteristics of the traffic condition data. With the data of Chengdu, it is demonstrated that two non-base states between successive base states is the most appropriate pattern for BSAMf in terms of data storage and accessing efficiencies. In sum, the experiment results demonstrate that the proposed BSAM for traffic condition data is effective and efficient.