## 1 Introduction
There are also many relevant studies analyzing taxi trajectory data under different application contexts. For example, [PERSON] et al. [13] analyzed taxi trajectory data to construct interaction relationships between local regions, and then applied the result to assist in city planning. [PERSON] et al. [14] and [PERSON] et al. [15] tried to extract the operation status of a traffic system from taxi trajectory data. To identify the city structure, [PERSON] et al. [16] proposed a field-based data clustering analysis method to detect the changing patterns of constant hotspot areas and inconstant hotspot areas. Since the pick-up and drop-off points of taxi trajectories often imply facilities of interest, [PERSON] et al. [17] used taxi trajectory data to discover the attractive areas that people often visit, e.g., hot shopping and leisure land uses or living and working areas. Recently, [PERSON] et al. [18] proposed an approach to identify traffic congestion regions and their spatiotemporal distributions from taxi trajectory data. [PERSON] et al. [19] viewed the trips of taxis as a displacement in the random walk model, and found that the distribution of directions of taxi trajectories in Shanghai shows a characteristic northeast east-southwest west dominant direction. In addition, they implemented the Monte Carlo simulation and found that geographical heterogeneity leads to a faster observed decay of trips, and the distance decay effect makes the spatial distribution of trips more concentrated in the urban area. [PERSON] et al. [20] proposed the use of spatially embedded networks and network analysis techniques to model intra-city spatial interactions. [PERSON] et al. [21] allocated Origin/Destination points to land use parcels for describing regional activities, and then combined a series of relevant indicators to explore the land use patterns of Wuhan city. [PERSON] et al. [22] analyzed the characteristics of the pick-up/drop-off points extracted from taxi GPS trajectories, and, based on these features of pick-up/drop-off points, extracted the regular patterns that correspond to the land-use classes within different regions in Hangzhou city. In addition to these aspects, taxi trajectory data can be also used for human mobility pattern mining [5; 23; 24] and environmental pollution analysis [25; 26]. Most previous studies have focused on the connectivity and aggregation of land uses, and few studies have been conducted to comprehensively understand the spatio-temporal community structures of a transport system. With continuous transportation development, there is an increasingly urgent demand for analysis of the spatial structures of transport flows and their variations across time. In general, a traffic flow can be considered a link that connects two land uses, and thus, with multiple flows of this type, a transport system can be modeled as a weighted graph. In this regard, we can introduce community detection and graph matching methods to explore the complex organization of land uses in a transport flow system [27; 28]. In this study, we propose a three-levels framework to mine intra-city vehicle trajectory data and detect the spatio-temporal relationships between land uses in the traffic flow system. Therefore, the main contributions of this study are the following:  
* Modeling the connectivity structure of traffic flows. We first constructed a spatially embedded network to model the connectivity of land uses, in which the node represents the partitioned region, the edge represents the linkage between adjacent nodes, and the weight of the edge depends on the volume of traffic flows between the corresponding nodes. * Extracting the aggregation patterns of land uses (i.e., nodes). Based on the network traffic flow, we then employed a community detection technique (i.e., K-Medoids clustering) to classify all the nodes. Instead of the simple geographic distance, our community detection method takes into account the real traffic volume and graph structure properties.