Development of Global Quality Index of Unpaved Roads

Abstract

Unpaved roads often are in poor condition, especially in developing countries, due to limited resources, resulting in discomfort, accident risk, vehicle operating costs (VOCs), freight transport damage, and difficulties accessing essential services by rural populations. Previous studies proposed methods that do not enable a complete and cost-effective unpaved road evaluation in the context of an integrated unpaved road management system (URMS). Developing global condition indexes is crucial in establishing a hierarchical classification of the whole road network, rationalizing resource allocation, and facilitating planning maintenance and rehabilitation candidate projects within a medium to long timeframe. The Global Quality Index of Unpaved Roads (GQIUR) development in this work provides data for management at the network and project levels, allowing the evaluation of riding quality and both distress at specific road sections. The GQIUR combines the Ride Quality Index of Unpaved Roads (RQIUR), proposed in this study, with the Unsurfaced Road Condition Index (URCI). By capturing data from cameras, accelerometers, and Global Positioning System (GPS) sensors on smartphones affixed to a vehicle’s windshield, it is possible to determine the RQIUR by evaluating ride quality while traveling. Recording surface distresses during walking surveys allows URCI calculation. The URCI and RQIUR classification was established based on the existing literature and the practicality of URMS. The GQIUR incorporates these attributes in a balanced manner, considering the comparable importance of the URCI and RQIUR for managers and users. The evaluation covered more than 10 km of unpaved roads. Asphalt and cobblestone pavement samples were compared with unpaved road data. GIS application to the GQIUR shows the general classification of unpaved roads. Unpaved roads present structural and functional distresses, and gravel roads cause excessive vibrations due to roughness and loose aggregates. The method enables priority section identification in a practical, objective, and cost-effective manner.