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Algorithm for Spatial Clustering of Pavement Segments
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DOI：10.1111/j.1467-8667.2008.00573.x.png)
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摘要
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An algorithm is developed to enable the Georgia Department of Transportation (GDOT) to determine pavement preservation project termini by analyzing segment-level pavement condition rating. This article formulates a new spatial search model for determining appropriate pavement preservation project termini. A spatial clustering algorithm using fuzzy c-mean clustering is developed to minimize the rating variation in each cluster (project) of pavement segments while considering minimal project scope (i.e., length) and cost, initial setup cost, and barriers, such as bridges. A case study using the actual roadway and pavement condition data in fiscal year 2005 on Georgia State Route 10 shows that the proposed algorithm can identify more appropriate segment clustering scheme, than the historical project termini. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed.
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