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Taking a representative metro station in Beijing as example, this research has newly developed a random coefficient model to predict the short-term passenger flows with sudden increases sometimes into an urban rail transit station. The hierarchical Bayesian approach is iteratively applied in this work to estimate the new model and the estimation outcomes in each of the iterative calibrations are improved by sequential Bayesian updating. It has been proved that the estimation procedure is able to effectively converge to rational results with satisfying accuracies. In addition, the model application study reveals that besides sufficient preparations in manpower, devices, etc.; the information of the factors affecting the passenger flows into an urban rail transit station should be timely transferred in advance from important buildings, road intersections, squares and so on in neighborhood to this station. In this way, this station is able to cope with the unexpectedly sharp increases of the passenger flows into the station to ensure its operation safety.

In this paper, a new design optimization methodcross entropy methods for passenger flow routing in passenger hubs is employed, in order to develop rational and efficient passenger flow routing program, which will help improving the passenger flow organization. According to the description and characteristics of the problem, we transform the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and can be well applied in passenger flow routing design problem.Systematic studying the passenger terminal passenger flow routing design optimization techniques and methods and developing rational and efficient passenger flow routing program will help improving the organization of passenger flow. According to the description of the problem and the characteristics of itself, the paper transforms the problem into a combinatorial optimization problem, so that it is convenient to explore the best solution. The paper also employs the cross entropy method to solve the problems. Numerical example declares that the method given above can obtain the optimal solution under the condition of fixed demand. Results show that the cross entropy method is effective, and this method can be well applied in passenger flow routing design problem.

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