  The movement of humans and goods in cities can be represented by constrained
flow, which is defined as the movement of objects between origin and
destination in road networks. Flow aggregation, namely origins and destinations
aggregated simultaneously, is one of the most common patterns, say the
aggregated origin-to-destination flows between two transport hubs may indicate
the great traffic demand between two sites. Developing a clustering method for
constrained flows is crucial for determining urban flow aggregation. Among
existing methods about identifying flow aggregation, L-function of flows is the
major one. Nevertheless, this method depends on the aggregation scale, the key
parameter detected by Euclidean L-function, it does not adapt to road network.
The extracted aggregation may be overestimated and dispersed. Therefore, we
propose a clustering method based on L-function of Manhattan space, which
consists of three major steps. The first is to detect aggregation scales by
Manhattan L-function. The second is to determine core flows possessing highest
local L-function values at different scales. The final step is to take the
intersection of core flows neighbourhoods, the extent of which depends on
corresponding scale. By setting the number of core flows, we could concentrate
the aggregation and thus highlight Aggregation Artery Architecture (AAA), which
depicts road sections that contain the projection of key flow cluster on the
road networks. Experiment using taxi flows showed that AAA could clarify
resident movement type of identified aggregated flows. Our method also helps
selecting locations for distribution sites, thereby supporting accurate
analysis of urban interactions.
