Detecting Pattern Changes in Individual Travel Behavior Based on a Bayesian Method

This paper focuses on the long-overlooked phenomenon that individual travel patterns are not always stable over the long term and may change due to seasonal changes, moving, and work schedule changes. Unlike previous studies that identified sudden peak points, this paper treats travel pattern change as a change point detection problem in a time series and defines change as “sudden, substantial, and continuous”. Considering the complexity of travel behavior, this paper measures changes in travel patterns in three dimensions: time, space, and frequency, and establishes a Bayesian change point detection model. A nine-month period of private car GPS data from Aichi, Japan, is used for an example analysis. The results show that the Bayesian approach can effectively identify travel pattern changes. Compared with the traditional GLR, the proposed method in this paper has higher recognition accuracy with lower model complexity. Meanwhile, the experimental results show that individual travel patterns may change in only one dimension or in multiple dimensions at the same time. Based on this, the correlation analysis of travel patterns in the temporal and spatial dimensions is carried out, and it is verified that there is a certain positive correlation between the two. The Bayesian change-point detection model proposed is robust and generally applicable to other fields besides travel patterns.