  To date, pavement management software products and studies on optimizing the
prioritization of pavement maintenance and rehabilitation (M&R) have been
mainly focused on three parameters; the pre-treatment pavement condition, the
rehabilitation cost, and the available budget. Yet, the role of the candidate
projects' spatial characteristics in the decision-making process has not been
deeply considered. Such a limitation, predominately, allows the recommended M&R
projects' schedule to involve simultaneously running but spatially scattered
construction sites, which are very challenging to monitor and manage. This
study introduces a novel approach to integrate pavement segments' spatial
coordinates into the M&R prioritization analysis. The introduced approach aims
at combining the pavement segments with converged spatial coordinates to be
repaired in the same timeframe without compromising the allocated budget levels
or the overall target Pavement Condition Index (PCI). Such a combination would
result in minimizing the routing of crews, materials and other equipment among
the construction sites and would provide better collaborations and
communications between the pavement maintenance teams. Proposed herein is a
novel spatial clustering algorithm that automatically finds the projects within
a certain budget and spatial constrains. The developed algorithm was
successfully validated using 1,800 pavement maintenance projects from two
real-life examples of the City of Milton, GA and the City of Tyler, TX.
