  This study considers that the collective route choices of travelers en route
represent a resolution of their competition on network routes. Well
understanding this competition and coordinating their route choices help
mitigate urban traffic congestion. Even though existing studies have developed
such mechanisms (e.g., the CRM [1]), we still lack the quantitative method to
evaluate the coordination penitential and identify proper coordination groups
(CG) to implement the CRM. Thus, they hit prohibitive computing difficulty when
implemented with many opt-in travelers. Motived by this view, this study
develops mathematical approaches to quantify the coordination potential between
two and among multiple travelers. Next, we develop the adaptive centroid-based
clustering algorithm (ACCA), which splits travelers en route in a local network
into CGs, each with proper size and strong coordination potential. Moreover,
the ACCA is statistically secured to stop at a local optimal clustering
solution, which balances the inner-cluster and inter-cluster coordination
potential. It can be implemented by parallel computation to accelerate its
computing efficiency. Furthermore, we propose a clustering based coordinated
routing mechanism (CB-CRM), which implements a CRM on each individual CG. The
numerical experiments built upon both Sioux Falls and Hardee city networks show
that the ACCA works efficiently to form proper coordination groups so that as
compared to the CRM, the CB-CRM significantly improves computation efficiency
with minor system performance loss in a large network. This merit becomes more
apparent under high penetration and congested traffic condition. Last, the
experiments validate the good features of the ACCA as well as the value of
implementing parallel computation.
