A hybrid approach for the analysis and prediction of elevator passenger flow in an office building

In an Elevator Group Control System (EGCS), the analysis and prediction of elevator traffic can improve service quality and system performance. For this purpose, we propose a new hybrid approach to analyze and predict elevator passenger flow. In this approach, nonlinear analysis methods are used to reveal the internal dynamic characteristics of passenger flow time series collected from an office building. The results suggest that passenger flow has obvious fractal and chaos characteristics. Based on these characteristics, the support vector machine (SVM) method and fuzzy information granulation (FIG) method are employed to predict passenger flow. The simulation results suggest that the accuracy of passenger flow prediction can meet the identification requirements of elevator traffic patterns in an EGCS. Therefore, the proposed approach can effectively address the passenger flow analysis and prediction problems of office buildings and the results can be used as a foundation for practical application in an EGCS.