Urban Travel Chain Estimation Based on Combination of CHMM and LDA Model

Abstract

Understanding travel patterns and predicting travel destinations has gained significant attention in the field of transportation research. This study proposes a methodology that utilizes continuous hidden Markov models (CHMMs) to estimate activity sequences for each travel chain and employs a travel destination prediction model based on a random forest (RF) model. Furthermore, it explores the optimization of the results from HMM using the latent Dirichlet allocation (LDA) model and applies it in predicting travel destinations. In the experiment, the dataset collected from unique travellers in Seoul city, South Korea, is used to validate the proposed model, which includes time stamps of origin and destination, location, travel mode and transfer nodes. Research findings show that during the modelling phase of the continuous hidden Markov model, the Gaussian mixture model categorizes the feature vectors into eight distinct groups. The estimated membership probability indicates involvement in four different activities. It also explains the relationship between derived activities. Finally, given the observed features, the proposed model provides an effective method for estimating the most likely sequence of activities in the travel chain. The results can help conduct further activity-based traffic demand analysis and improve the service quality of the transportation system.