A hybrid data mining method is proposed for the analysis of large sets of pavement management systems data.
An optimal arrangement method of surveyed inspection units to minimize inspection errors is developed.
Hybrid particle swarm optimization- and genetic algorithm-based methods are considered to obtain optimal results.
Application of the hybrid method is presented at network, project and section levels.