  Detecting regional spatial structures based on spatial interactions is
crucial in applications ranging from urban planning to traffic control. In the
big data era, various movement trajectories are available for studying spatial
structures. This research uses large scale Shanghai taxi trip data extracted
from GPS-enabled taxi trajectories to reveal traffic flow patterns and urban
structure of the city. Using the network science methods, 15 temporally stable
regions reflecting the scope of people's daily travels are found using
community detection method on the network built from short trips, which
represent residents' daily intra-urban travels and exhibit a clear pattern. In
each region, taxi traffic flows are dominated by a few 'hubs' and 'hubs' in
suburbs impact more trips than 'hubs' in urban areas. Land use conditions in
urban regions are different from those in suburban areas. Additionally, 'hubs'
in urban area associate with office buildings and commercial areas more,
whereas residential land use is more common in suburban hubs. The taxi flow
structures and land uses reveal the polycentric and layered concentric
structure of Shanghai. Finally, according to the temporal variations of taxi
flows and the diversity levels of taxi trip lengths, we explore the total taxi
traffic properties of each region and proved the city structure we find.
External trips across regions also take large proportion of the total traffic
in each region, especially in suburbs. The results could help transportation
policy making and shed light on the way to reveal urban structures with big
data.
