Automatic urban road network extraction from massive gps trajectories of taxis

Urban road networks are fundamental transportation infrastructures in daily life and essential in digital maps to support vehicle routing and navigation. Traditional methods of map vector data generation based on surveyor's field work and map digitalization are costly and have a long update period. In the Big Data age, large-scale GPS-enabled taxi trajectories and high-volume ride-sharing datasets are increasingly available. These datasets provide high-resolution spatiotemporal information about urban traffic along road networks. In this study, we present a novel geospatial-big-data-driven framework that includes trajectory compression, clustering, and vectorization to automatically generate urban road geometric information. A case study is conducted using a large-scale DiDi ride-sharing GPS dataset in the city of Chengdu in China. We compare the results of our automatic extraction method with the road layer downloaded from OpenStreetMap. We measure the quality and demonstrate the effectiveness of our road extraction method regarding accuracy, spatial coverage and connectivity. The proposed framework shows a good potential to update fundamental road transportation information for smart-city development and intelligent transportation management using geospatial big data.