Enhancing digital road networks for better transportation in developing countries

Data scarcity in developing countries often poses significant challenges to the use of analytics in addressing development issues. In transportation research, digitized road data is one of the most fundamental data structures, and a poorly digitized road network significantly reduces the ability to optimize trade of micro-enterprises (SDG 8) and placement of hospitals (SDG 3). Unfortunately, current methods to enhance or create digital road networks are not well-adapted to regions with sparse geospatial data, often resulting in poor digital representations of road networks in less-developed regions such as rural areas of developing countries. We present a novel projection-based incremental insertion method that is well-suited to either enhance large existing road networks or combine multiple sources of road networks in regions with sparse geospatial data. In collaboration with PemPem and the World Bank, we perform two case studies that demonstrate the effectiveness of the proposed method. Together with PemPem, we show that our method significantly improves the digital road network for smallholder farmers in Indonesia, where only 40% of the origin–destination pairs in our dataset were previously digitized. Moreover, in a case study of optimizing geospatial accessibility to healthcare in Timor-Leste (World Bank), the improved digital road network detects an additional 5% of people to be in the vicinity of a hospital.