Geospatial-based decision support system for prioritizing road segments for maintenance and rehabilitation

Given the growing demand for moving goods and people and the escalating number of vehicles on highways, issues related to road safety have become common concerns. Additionally, the aging of the road infrastructure and the intensification of climatic change effects reinforce the problem of road pavement serviceability. However, accessing funds for road maintenance and having the autonomy for pure technical decisions remain significant challenges for transport authorities. Thus, this paper addresses developing a geospatial-based systematic process for prioritizing highway segment candidates for maintenance and rehabilitation. The investigation was designed to facilitate the decision-making process for the state transportation authority to allocate funds over the 20.315 km of major highways in Minas Gerais, Brazil. The method consisted of ranking the highway segments regarding the geographic context to which each segment belongs, as well as the operational characteristics of the highways. The approach meets the United Nations Sustainable Development Goals by considering socioeconomic and demographic perspectives along with other traditional engineering characteristics such as the available transport infrastructure, the traffic volume, the highway capacity, the pavement serviceability and the potential for deterioration, the condition of the surface drainage, and weather-related data. Transportation practitioners with expertise in pavement maintenance and rehabilitation and transportation managers contributed to the development by weighting the factors according to the best possible technical judgment regarding prioritizing funds. The GIS-oriented Delphi framework resulted in a priority index. The index allowed the elaboration of a ranking of priorities, which is vital information regarding public policies and the transparent use of public money for road maintenance and rehabilitation within the State of Minas Gerais. The model is robust and outputs geoinformation and tabular data to feed the decision-making process in an organized and transparent manner. Therefore, the core methodology can be easily transferable to other jurisdictions.