Improving vehicle speed prediction transferability with network centrality

Even though we are currently witnessing an unprecedented growth in the collection of movement data, practitioners in many fields still struggle with gaining access to reusable mobility data, such as traffic flows and speeds. Data availability varies considerably between different cities and regions. While some publish comprehensive open datasets, others either do not provide their data or do not even posses any traffic data. This paper proposes a solution to the problem of missing vehicle speed data. Our approach is to train a prediction model in an area where data is available and then transfer this model to areas where data is lacking. The proposed method requires only readily available static road network data in the target area. We improve upon previously published prediction models by incorporating local network centrality measures. Our approach reduces errors in vehicle speed prediction by as much as 24%.