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.
Highlights
R Following the acceleration of urbanization, the A contradiction between traffic supply and demand increased seriously, and this contradiction cannot be solved fundamentally just by infrastructure construction which canD improve the traffic supply capacity
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
Management (TDM) is the effective way to solve the contradiction between traffic supply and demand [1]
Summary
R Following the acceleration of urbanization, the A contradiction between traffic supply and demand increased seriously, and this contradiction cannot be solved fundamentally just by infrastructure construction which can. We propose a Bayesian network model to address travel mode choice problem and discover how trip makers’ travel decision behaviors are affected by underlying socioeconomic and level-of-service factors. Bayesian network can provide great convenience for probabilistic reasoning This is mainly because that on one side Bayesian network is a strict language of mathematics which fits for computer processing; on the other side, the intuitive and understandable features either trip-based or activity-based travel demand modeling make it easy for discussion and to build model. Bayesian parameter estimation could solve factors set, including travel-activities decisionsuch problem effectively. City in southeastern China residents' travel mode choice as case study to apply Bayesian methodology in real travel decision behavior analysis. Parameter learning means to determine the parameters when we know network structure; Structure learning is to make sure the network structure, and to determine the parameters
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