  Network constrained points are referred to as points restricted to road
networks, such as taxi pick up and drop off locations. A significant pattern of
network constrained points is referred to as an aggregation; e.g., the
aggregation of pick up points may indicate a high taxi demand in a particular
area. Although the network K function using the shortest path network distance
has been proposed to detect point aggregation, its statistical unit is still
radius based. R neighborhood, in particular, has inconsistent network length
owing to the complex configuration of road networks which cause unfair counts
and identification errors in networks (e.g., the length of the r neighborhood
located at an intersection is longer than that on straight roads, which may
include more points). In this study, we derived the length L function for
network constrained points to identify the aggregation by designing a novel
neighborhood as the statistical unit; the total length of this is consistent
throughout the network. Compared to the network K function, our method can
detect a true to life aggregation scale, identify the aggregation with higher
network density, as well as identify the aggregations that the network K
function cannot. We validated our method using taxi trips pick up location data
within Zhongguancun Area in Beijing, analyzing differences in maximal
aggregation between workdays and weekends to understand taxi demand in the
morning and evening peak.
