Inferring Road Intersection Control Type from GPS Data

Abstract

Transport modelling requires accurate and usually hard to find intersection control rules. The widespread of smartphone applications enabled the automatic collection of road network-related data that can contribute to and improve transport modelling. Global Positioning System (GPS) point data collected in Quebec City, Canada, was used to develop a model inferring intersection control type (traffic light, stops on all approaches, or stops on the secondary approach). Data was used to train and validate supervised machine learning classification models. The developed model predicted intersection control types on a validation dataset with a 96% accuracy. This work presents the best predictors for intersection control type.