Short-term travel flow prediction method based on FCM-clustering and ELM

Abstract

Short-term travel flow prediction has been the core of the intelligent transport systems (ITS). An advanced method based on fuzzy C-means (FCM) and extreme learning machine (ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.