Optimizing Pavement Inspection Planning with the Help of Traffic Volume Forecasting

Although many studies have tried to improve the current pavement management inspection system, this improvement is temporary and requires subjective judgment; they do not assess the risk of sudden inspection and do not consider the physical reason in predicting the condition of the pavement. The aim of this paper is to make full use of the large collection of traffic information from ITS technologies and high-accuracy flow prediction models. Using these resources, it is possible to implement a more mechanistic model for pavement condition prediction that accommodates variables that contribute to the failure process, such as traffic characteristics, structural characteristics of pavements, and environmental factors. Therefore, considering the weakness of the previously presented methods, this paper proposes a framework for optimizing a flexible inspection schedule, the main objective of which is to prevent the sudden inspection risk from predicting the pavement condition while minimizing the inspection life cycle cost. The obtained results show that flexible inspection performs better than conventional inspections in terms of total efficiency, which includes interval efficiency, distribution efficiency, and risk penalty, and the overall efficiency of this type of inspection is relatively stronger than other inspection methods.
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