Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
