This paper reports the classification of 90 sample pavilions in Shanghai World Expo. An artificial intelligence based nonlinear clustering method known as Self-Organizing Map(SOM) has been used to classify expo pavili...This paper reports the classification of 90 sample pavilions in Shanghai World Expo. An artificial intelligence based nonlinear clustering method known as Self-Organizing Map(SOM) has been used to classify expo pavilions. SOM is an efficient tool for visualization of multidimensional data. To conduct the classification, four characteristics namely Hurst exponent for queue length, Hurst exponent for waiting time, mean queue length and mean waiting time have been applied. The classification results show that Shanghai World Expo pavilions can be optimally classified into four classes. This result will shed light on further studies that how to manage the queue of World Expo pavilions in the future.展开更多
基金supported by 973 Research Program under Grant No.2010CB731500the National Natural Science Foundation of China under Grant Nos.71403262,91024010,91324009+1 种基金Innovative Team Program under Grant No.GH13041Major Program of Institute of Policy and Management,Chinese Academy of Sciences under Grant No.Y201201Z06
文摘This paper reports the classification of 90 sample pavilions in Shanghai World Expo. An artificial intelligence based nonlinear clustering method known as Self-Organizing Map(SOM) has been used to classify expo pavilions. SOM is an efficient tool for visualization of multidimensional data. To conduct the classification, four characteristics namely Hurst exponent for queue length, Hurst exponent for waiting time, mean queue length and mean waiting time have been applied. The classification results show that Shanghai World Expo pavilions can be optimally classified into four classes. This result will shed light on further studies that how to manage the queue of World Expo pavilions in the future.
