A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

Abstract

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user's personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi'an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers' characteristics and trip characteristics.

Application of Probabilistic Graphical Models in Forecasting Crude Oil Price

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Traffic Structure Optimization in Historic Districts Based on Green Transportation and Sustainable Development Concept

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