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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]

The storage allocation problem of outbound containers in container yard is usually optimized by two successive stages, yard-bay allocation problem (YBAP) and space allocation problem (SAP), but they cannot obtain global optimal solution. This paper proposes an outer–inner cellular automaton algorithm (CAOI) to solve this problem, using YBAP and SAP as an integrated optimization process. Our work can be divided into several parts: (1) The outer cellular automaton (CAO) model is established to solve YBAP, and the inner cellular automaton (CAI) model is established to solve SAP. (2) In CAO model, an End–Front–Front–End (EFFE) transition rule is designed to make a cell change its state more instructive. (3) In CAI model, cells are initialized through a branch and bound method based on least-cost priority queue (LCBB). LCBB makes initial CAI cell space in an approximate optimal state. (4) An integrated strategy for CAO and CAI model is proposed, by which CAO and CAI execute co-evolution under the control of CAOI. Extensive experiments show CAOI achieves a better performance in solving storage allocation problem than other state-of-the-art methods, which synchronously optimizes the container transportation distance, the number of allocated yard-bays and rehandles. [ABSTRACT FROM AUTHOR]