Road surface reflection tables ( r-tables) relate scene illuminance to luminance seen by a car driver. They are important for many road lighting tasks accounting for road optical properties, such as new illumination design, new pavement texture or lighting design software, to reduce energy consumption without losses on safety and visibility. This paper aims first at finding a space of basis functions to describe r-tables. From a database of 34 r-tables covering a large variety of pavements, a principal component analysis allows to construct a 33-dimensional space, basis for r-table representations. From that statistical model, a method is exposed to retrieve r-table from a luminance map. The estimated r-table is then used to calculate a reconstructed luminance map. Road lighting quality criteria are also derived and they demonstrate the relevancy of the estimated r-table. Finally the model is tested with noisy input data and it remains stable and reliable, making it applicable with experimental luminance maps.