The emergence of new technologies allows a better understanding of traffic conditions and urban networks dynamics. The Bluetooth technology is becoming widespread, as it represents a cost-effective means for measuring travel times, in both arterial roads and motorways. Although the extraction of travel time from the Bluetooth data is fairly straightforward, data processing is still challenging with the issues of penetration rate, mode discrimination and detection quality. This paper presents a methodological contribution to the filtering and analysis of the Bluetooth data, over a large urban network (Brisbane, Australia). Our approach introduced the concept of B-OD matrix (Bluetooth Origin-Destination Matrix), which we build from a network of 79 Bluetooth detectors (see FIGURE 1), located within the Brisbane urban area. The B-OD matrix describes the dynamics of a sub-population of vehicles, between pairs of detectors. The results show that the dynamics of the urban networks can be effectively represented through B-OD matrices. A comparison with loop detector data enabled us to verify that the B-OD matrices represent the dynamics of a significant sample of the entire population of vehicles. A spatiotemporal clustering analysis was also performed, through two different methods. These are LDA (Latent Dirichlet Allocation) and K-means. LDA was used to highlight temporal pattern; while the K-means clustering was used to identify different Bluetooth Fundamental Diagrams (B-FD) classes. The results show that the Bluetooth Data is a reliable data source, that can be utilized to effectively cluster various traffic conditions, through spatiotemporal networks structures.