Speed Limits Can Be Determined from Geospatial Data with Machine Learning Methods.

Various country-wide guidelines for speed limits setting make use of diverse parameters specifying properties of roads, their environment, as well as traffic intensity, statistical distribution of measured vehicle speeds and history of crash accidents. We argue, that although such extensive data are not present in public geospatial datasets, they can be inferred as latent features in machine learning models trained to predict speed limits. To verify this hypothesis we performed an experiment, in which we extracted tag-based and geometrical features from Open Street Map for Poland and applied various classification methods to predict class labels corresponding to speed limits. In spite of the fact that the datasets were imbalanced (with majority classes corresponding to default speed limits) we obtained F1 scores ranging from 0.72 to 0.91 for lower speed roads. The neural network classifier implemented with Tensorflow framework turned out to be the most efficient.