## 1 Introduction  
Transport systems are of great importance to urban environments for their connectivity, aggregation, and dynamic functions. Land uses are connected by the transport network to improve the accessibility of human activities. Considering the spatial heterogeneity of traffic flows, multiple land uses are also attracted by each other, showing an agglomeration (or aggregation) pattern in the space. In addition, such characteristics of a transport system depend largely on the temporal dimension. Therefore, mining the connectivity, aggregation, and dynamic patterns of transport flows can be helpful for revealing traffic structures and the associated mechanisms of socioeconomic phenomena, e.g., logistics, neighborhood, living habitats, and urban function zones [1; 2; 3]. In reality, the regionalization of urban areas is often non-adjacent. For example, working areas and residential areas belong to the same group in terms of their functions, while in the physical space, they are often distant from each other. Therefore, only using geometric indicators such as geographic distance to measure the connectivity of land uses of interest is limited, and the potential solution could come from the function space of transport. Instead of the static condition of geometric space, a transport system implies the real interactions between land uses across space and time. For example, in the morning, the interaction between residential areas and working areas is much intense, while at lunch time, the interaction between working areas and catering service areas is more intense. In addition, the interactions of land uses in terms of traffic flows can reveal the traffic conditions on different routes in the urban space. It is believed that different routes serve different roles in the daily transportation. For example, the routes connecting residential areas and working areas are likely to be chosen by commuters in the morning. Besides the main roads, some minor roads, for example detours, could also be favored by residents. However, how to extract the functional associations between land uses remains a challenging task. Considering the importance of transport systems, many studies have been done to discover the hidden regularities in urban transportation. However, due to limited data sources, it is rather difficult to identify the changes of traffic flows across space and time [1; 4]. In addition, China's transport infrastructures are developing very fast, and the associated transport systems are becoming more complex and dynamic. It is necessary to study the transport system in a more effective and timely way. Benefiting from the recent development of location-aware sensing technologies, there is an unprecedented opportunity for us to obtain big traffic data with agent trajectory information [5; 6]. For example, nowadays, most of vehicles are equipped with a global positioning system (GPS), which records the locations and other semantic properties (e.g., speed and direction) of agents. Analyzing these data within the context of a transport system could provide a detailed view of the traffic flow, and then reveal typical spatial interaction patterns in the urban space, e.g., popular routes for driving, associations among land uses, and the spatial structures of urban space. For example, [PERSON] et al. [7] used mobile phone positioning data to explore the movement patterns of suburban commuters in Tallinn, Estonia. They found that there is a remarkable temporal rhythm to respondents' locations. Based on mobile phone data, [PERSON] et al. [8] also discovered that there is significant temporal regularity in human mobility. Other data sources can be also used to analyze traffic flows, e.g., location data of buses [9], smart card transaction data of subways [10], and taxi trajectory data [11; 12]. Compared to other modes of transport, the taxi trajectory has no limitations of a fixed line, and thus is more flexibly able to reflect real traffic flows in an urban environment.