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The continuous expansion of the urban traffic sensing infrastructure has ledto a surge in the volume of widely available road related data. Consequently,increasing effort is being dedicated to the creation of intelligenttransportation systems, where decisions on issues ranging from city-wide roadmaintenance planning to improving the commuting experience are informed bycomputational models of urban traffic instead of being left entirely to humans.The automation of traffic management has received substantial attention fromthe research community, however, most approaches target highways, producepredictions valid for a limited time window or require expensive retraining ofavailable models in order to accurately forecast traffic at a new location. Inthis article, we propose a novel and accurate traffic flow prediction methodbased on symbolic regression enhanced with a lag operator. Our approachproduces robust models suitable for the intricacies of urban roads, much moredifficult to predict than highways. Additionally, there is no need to retrainthe model for a period of up to 9 weeks. Furthermore, the proposed methodgenerates models that are transferable to other segments of the road network,similar to, yet geographically distinct from the ones they were initiallytrained on. We demonstrate the achievement of these claims by conductingextensive experiments on data collected from the Darmstadt urbaninfrastructure.

Traffic Modelling and Prediction via Symbolic Regression on Road Sensor Data