Pedestrian crowd in public buildings is usually a hidden trouble. The more serious the crowd, the higher the accident risk, and the heavier the casualties and loss may be. Based on the social force model, this paper puts forward an improved model which takes into account the anisotropic characteristic of pedestrian movement and the avoidance of dynamic congested areas. Furthermore, the algorithm has been optimized with the help of static force grid, so the simulation becomes more veritable and computational speed is also greatly accelerated. The improved model is also applied to research on the characters of pedestrian crowd, and the conclusion can provide a basis for the risk assessment of pedestrian crowd and the design of public buildings. social force model, anisotropic behavior, the locality principle, pedestrian crowd

Predictive analysis about China RailwayParcel is an urgent task for Railway Intelligent Transportation System (RITS). This paper proposes a novel parcel traffic volume predictive method named FCP (Fuzzy Cell Prediction), which provides an effective means to build strategic models and enhances the predictive ability of interactive and hierarchical analysis. The Key components of FCP are traffic data process mode which map variable to infinite spaces. The variable multiple correlation problem are solved. Applications to maximize transport capacity suggest that FCP is a contributive and powerful tool for the RITS.

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