In order to improve traffic conditions and reduce carbon emissions in urban areas, smart mobility and smart cities are becoming increasingly important measures. To enable the widespread use of the cameras required for this, cost and size requirements necessitate the use of low-cost standard dynamic range (SDR) cameras. However, these cameras do not provide sufficient image quality for a reliable classification of road users, especially at night. In this paper, we present a data-driven approach to optimise image quality and improve classification accuracy of a given vehicle classifier at night. Our approach uses a combination of image inpainting and high dynamic range (HDR) image reconstruction to reconstruct and optimise critical image areas. Therefore, we introduce a large HDR traffic dataset with time-synchronised SDR images. We also present an approach to automatically degrade the HDR traffic data to generate relevant and challenging training pairs. We show that our approach significantly improves the classification of road users at night without having to retrain the underlying vehicle classifier. Supplementary information as well as the dataset are published at https://www.mt.hs-rm. de/ nighttime-traffic-reconstruction/ .