An increase in air traffic demand, aging pavement infrastructure, and limited funds make pavement management decision making a difficult process. Pavement management systems (PMS) are essential for pavement management. PAVER, the most common airport pavement management system (APMS) assists pavement managers by identifying the surface condition of pavement sections, however it’s based only on the pavement condition index (Greene, Shahin, & Alexander, 2004). Other criteria are taken into account by pavement managers to select the pavement sections that will be maintained or rehabilitated; however these criteria are not included in PAVER or any APMS. For pavement managers, it is very difficult to select the pavement sections that will be repaired because not all the sections in need of M&R can be selected. This research has focused on developing a pavement management decision making tool to help pavement managers prioritize and identify the sections that need M&R, based not only on PCI but on all relevant criteria needed for airport pavement management. The main research question to be answered in this research is: How can airport pavement management decision making be improved by means of data to identify the pavement sections that need to be maintained or rehabilitated? The chosen methodology to identify pavement sections in need of maintenance is the absolute Analytical Hierarchy Process (AHP). This research has explored the applicability of this methodology for prioritizing pavement sections in airports. Three major airports in Europe have been chosen as case studies for this research: Amsterdam Airport Schiphol, Brussels Airport, and Heathrow airport. Based on literature research and on these case studies, the required criteria used in airport pavement management for identifying pavement sections to be maintained have been identified. The required data for all criteria has also been identified. Based on all the criteria, the required data, and the AHP methodology, the mentioned tool has been designed. The design of the tool was presented to expert pavement managers to corroborate its applicability before its development. The tool has been developed in Microsoft Excel and its applicability has been illustrated by using partially real data and partially fictitious data. The AHP was successfully implemented allowing pavement managers prioritize large amounts of pavement sections, considering all relevant criteria. This research has revealed that pavement management decision making can be improved by means of data, by the development of tools like the one proposed in this research. It was also shown how big volumes of data can improve pavement management decision making, providing managers relevant information like the remaining structural life of pavement sections. Airports can benefit from this research by reducing the required time for prioritizing pavement sections and selecting those to be maintained or rehabilitated, by minimizing the amount of time spent on projects that will be disregarded, and by strengthening or facilitating the application of predictive maintenance.