Travel Mode Choice Modeling: A Comparison of Bayesian Networks and Neural Networks

Abstract

In recent years, Bayesian networks and neural networks have been widely applied to the travel demand prediction area. However, their prediction performance is rarely directly compared. By experimental tests conducted using the same dataset, a Bayesian network model and a neural network model are compared for the travel mode analysis for the first time in this paper. It is found that the fully Bayesian network model tends to overfit the training set when the network itself is considerable complicated. The TAN structure otherwise has a better generalization performance and can achieve a better and more stable prediction performance, for its prediction accuracy 75.4%±0.63%, compared to the BP neural network model ,which prediction accuracy is 72.2%±3.01%. Experiment and statistical tests demonstrate the superiority of Bayesian networks and we propose using Bayesian networks, especially TAN, instead of neural networks in the travel mode choice prediction field.