A Multi-Objective Optimization Approach for Low Volume Rural Road Maintenance Management Incorporating Socioeconomic Importance

Rural roads are essential for local communities as they play a key role in providing access to the market, education, health, etc. However, most of these roads, especially in developing countries, are not maintained efficiently by the road agencies mainly due to financial, technical resource constraints, among many others. Established pavement condition monitoring systems require high technical resources, and are often costly and time-consuming. Therefore, most local road agencies are relying on subjective, ad hoc decision-making, which is led to making sub-optimal resource allocation for pavement maintenance. These roads also serve the various socio-economic purposes of the community, and this needs to be considered in prioritizing roads for maintenance, in addition to the traditional pavement performance metrics. This study proposes a methodology to incorporate socio-economic importance of rural roads that can be adopted in both short-term planning and long-term performance evaluation. Pavement condition performance is evaluated using smartphone-based roughness data, while socio-economic importance consists of connectivity, community importance, land-use pattern, and the traffic volume. The analysis scheme incorporates both single and bi-objective optimization approaches using genetic algorithm-based solution process under a pre-defined decision tree. Finally, use of the developed analysis scheme has been demonstrated using a rural road network as a case study.