  The continuous expansion of the urban traffic sensing infrastructure has led
to a surge in the volume of widely available road related data. Consequently,
increasing effort is being dedicated to the creation of intelligent
transportation systems, where decisions on issues ranging from city-wide road
maintenance planning to improving the commuting experience are informed by
computational models of urban traffic instead of being left entirely to humans.
The automation of traffic management has received substantial attention from
the research community, however, most approaches target highways, produce
predictions valid for a limited time window or require expensive retraining of
available models in order to accurately forecast traffic at a new location. In
this article, we propose a novel and accurate traffic flow prediction method
based on symbolic regression enhanced with a lag operator. Our approach
produces robust models suitable for the intricacies of urban roads, much more
difficult to predict than highways. Additionally, there is no need to retrain
the model for a period of up to 9 weeks. Furthermore, the proposed method
generates models that are transferable to other segments of the road network,
similar to, yet geographically distinct from the ones they were initially
trained on. We demonstrate the achievement of these claims by conducting
extensive experiments on data collected from the Darmstadt urban
infrastructure.
