Research on Seasonal Index Based on Dynamic Clustering of the Daily Railway Passenger Flow Title

Abstract

The railway passenger flow is greatly impacted by different months and weeks of the season, and the impact is periodic. Accurate evaluation of the seasonal index for predicting the railway passenger flow is of key importance. Based on this background, the paper proposes an algorithm for calculating the seasonal index which is impacted by both months and weeks. The railway passenger flow between different OD(Origination Destination) is affected by months and weeks quite different. Therefore the paper focuses on the method for effective calculation of the month index and week index on the basis of time series clustering. When adopting hierarchical cluster, general Euclidean distance and its expansion used as a similarity metric is widely applied in time series comparison, however, this distance measurement is not robust enough for the processed data. Dynamic time warping is a pattern matching algorithm based on nonlinear dynamic programming technique. It is applied to calculate month and week index to get seasonal index that defined in this paper, which has good application value for predicting the passenger flow.