Abstract

In this paper, we study the travel mode choice of residents to determine the set of factors which can influence travel mode choice of residents and analyze the influence factor characteristics. Using Bayesian theory, we analyze the travel decision-making data of the residents, discrete them, and use them in Bayesian network structure learning and parameter estimation by K2 algorithm. We establish a Bayesian network simulation model to analyze the dependence probability relationship between the parent nodes and child nodes. Validation test was carried out for the building simulation model of Bayesian network. Data analysis results showed that the Bayesian network has a high accuracy prediction for actual travel mode choice of residents. This paper studies the Bayesian structure and parameters learning method for the actual travel behavior, and this method which provides a new method for studying the travel mode choice of residents can reveal the relationship between the various attributes associated with travel mode choice through a new perspective.