Genetic Programming with Transfer Learning for Urban Traffic Modelling and Prediction

Intelligent transportation is a cornerstone of smart cities' infrastructure. Its practical realisation has been attempted by various technological means (ranging from machine learning to evolutionary approaches), all aimed at informing urban decision making (e.g., road layout design), in environmentally and financially sustainable ways. In this paper, we focus on traffic modelling and prediction, both central to intelligent transportation. We formulate this challenge as a symbolic regression problem and solve it using Genetic Programming, which we enhance with a lag operator and transfer learning. The resulting algorithm utilises knowledge collected from other road segments in order to predict vehicle flow through a junction where traffic data are not available. The experimental results obtained on the Darmstadt case study show that our approach is successful at producing accurate models without increasing training time.