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The increasing number of vehicles in urban areas draws significant attention to traffic signal control (TSC), which can enhance the efficiency of the entire network by properly switching the phases of each signalized intersection. Fixed and max-pressure methods are commonly used in TSC systems owing to their high simplicity and good interpretability, but they respectively lack dynamic adaptability and automatic rule generation, possibly leading to low solution accuracy in complicated traffic environments. Meanwhile, meta-heuristic and black-box learning methods meet challenges in practice such as extensive computational time and poor interpretability. To this end, this paper proposes a new TSC method based on Genetic Programming (GP) to generate descriptive score rules automatically for switching phases of all signalized intersections in an urban transportation network. In the proposed method, switching …