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Urban traffic jams severely affect many cities around the world. Therefore, there is an urgent need for accurate prediction of traffic jams in real time. With many existing approaches, it is difficult to obtain favorable results for dynamic and uncertain traffic flows in urban networks. This paper proposes a real-time traffic jam prediction method based on a two-dimension cellular automaton model inspired by the well-known Biham, Middleton and Levine (BML) model. The proposed model combines the simplicity and high efficiency of the BML model with real urban traffic networks. Our work can be divided into two parts: (1) the proposal of a practical approach for mapping the urban traffic topological structure into a modified BML (M-BML) model, and (2) the development of a novel strategy to deal with different cells of the M-BML model, especially conflict points and fuzzy points, which facilitates the recognition of real traffic jams at intersections. To the best of our knowledge, this is the first time that real urban traffic road networks are directly mapped into a descriptive model for jam prediction. Extensive experiments confirm the accuracy and efficiency of the proposed M-BML model in predicting traffic jams in dynamic and uncertain real urban traffic networks. [ABSTRACT FROM AUTHOR]