Integrating Project Spatial Coordinates into Pavement Management Prioritization

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.

💡 Research Summary

The paper addresses a critical gap in contemporary pavement management systems (PMS) – the neglect of spatial relationships among candidate maintenance and rehabilitation (M&R) projects. Traditional PMS tools and most recent optimization models focus on three primary variables: the pavement condition index (PCI), the rehabilitation cost, and the available budget. Consequently, recommended project schedules often contain simultaneously active sites that are geographically scattered, leading to increased logistical complexity, higher mobilization costs, and difficulty in monitoring.

To overcome this limitation, the authors propose a novel spatial clustering algorithm that integrates geographic coordinates with budgetary and temporal constraints. The method treats each pavement segment as a point in Euclidean space, assigning it a cost value. The goal is to partition the entire set of points into a predefined number of clusters, each representing a fiscal year, such that the sum of costs within a cluster does not exceed the allocated budget for that year. The algorithm proceeds as follows:

1. Initial centroid selection – a point (often the lowest‑cost segment) is chosen as the seed of a new cluster.
2. Weight assignment – three criteria are weighted: (a) proximity to the centroid (highest priority), (b) individual project cost (lower cost favored), and (c) scheduled year (earlier years preferred).
3. Incremental neighbor addition – points are ordered by Euclidean distance from the centroid. The algorithm adds the nearest point to the cluster as long as the cumulative cost remains below the fiscal‑year budget.
4. Cluster finalization – once adding another point would breach the budget, the cluster is closed, its points are removed from the unassigned pool, and a new centroid is selected for the next cluster.
5. Iterative process – steps 1‑4 repeat until all points are assigned.

The authors distinguish their approach from classic k‑means, k‑medoids, and DBSCAN. While those methods either require a preset number of clusters without budget considerations (k‑means/k‑medoids) or rely on density parameters without cost constraints (DBSCAN), the proposed algorithm explicitly incorporates a monetary ceiling for each cluster, making it directly applicable to PMS planning.

A sub‑routine named “ClusterGrow” handles the ordered insertion of points, cost accumulation, and termination checks. The overall computational complexity is dominated by the distance‑sorting step (O(n log n)) and the iterative cluster formation, leading to a worst‑case O(n²) behavior, which is acceptable for the scale of typical municipal road networks but may require optimization for state‑wide applications.

Validation – The methodology was tested on 1,800 real M&R projects from two U.S. municipalities: Milton, Georgia, and Tyler, Texas. Both cities provided multi‑year budgets and a list of candidate projects with associated costs and GIS coordinates. The conventional PMS schedule for each city featured many projects dispersed across the network within the same fiscal year. After applying the spatial clustering algorithm, the average intra‑cluster distance decreased by roughly 30 % compared with the baseline schedule, indicating a more compact geographic grouping. Importantly, every cluster’s total cost stayed within the prescribed budget, and the number of fiscal years required to complete all projects remained unchanged, demonstrating that spatial consolidation did not compromise financial feasibility or overall planning horizon.

Critical appraisal – The study’s primary contribution lies in formalizing spatial proximity as a hard constraint alongside budget and time, a combination rarely addressed in pavement management literature. By doing so, it provides a practical decision‑support tool that can reduce crew travel time, fuel consumption, and coordination overhead. However, several limitations are evident:

• The cost weighting is static; real‑world projects often experience cost overruns or savings that could shift optimal cluster composition.
• The number of clusters is fixed to the number of fiscal years, which may be restrictive when budgets fluctuate or when a municipality wishes to compress the schedule.
• The algorithm’s quadratic worst‑case complexity could become prohibitive for very large networks (e.g., state‑wide road systems with tens of thousands of segments).

Future research directions suggested by the authors include dynamic cost weighting, integration of meta‑heuristic optimization (e.g., genetic algorithms, particle swarm) to explore a broader solution space, and parallel or cloud‑based implementations to improve scalability.

Conclusion – By embedding spatial clustering within the traditional PMS framework, the paper delivers a method that simultaneously respects budget caps, yearly planning horizons, and geographic efficiency. The empirical results from two municipalities substantiate the method’s ability to produce more compact, logistically manageable project groupings without exceeding financial limits. This contribution is valuable for transportation agencies seeking to enhance operational efficiency, reduce environmental impacts associated with vehicle travel, and improve the overall effectiveness of pavement maintenance programs.