Abstract:

In response to the lack of dynamic characterization of residents as the main body of urban spatial activities in traditional functional area identification methods， this paper proposes a functional attribute identification method for urban parcels based on taxi trajectory and POI data. Firstly， a class of departure and arrival time vectors are constructed respectively. Then， the residents' travel patterns is clustered by an improved dynamic time regularization and clustering algorithm. Finally， the functional attributes of the blocks are identified by combining the residents' travel curve characteristics， POI density and enrichment index. Taking Xi'an city as an example， the departure and arrival pattern characteristics of residents in different regions on weekdays and rest days are discussed to identify the functional attributes of different blocks within the city. The results show that different departure and arrival patterns represent different peaks in the morning peak， afternoon peak， evening peak， night and early morning， and the corresponding blocks show a certain circle structure in the spatial distribution and their respective functional tendencies. The identification of the functional attributes of the parcels using the departure-arrival pattern characteristics of residents and POI information has a complementary effect， and the functional attributes show a ternary structure of "employment-residence-rest"， which also reflects the spatial and temporal changes of different functional areas and people's activities. The results of the study are useful for planning departments to reallocate transport resources and optimize the spatial structure of the city.

Key words: transportation planning and management, time series clustering, taxi track data, residential travel characteristics, functional area identification