Abstract
The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.
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Acknowledgement
This research is funded by National Natural Science Foundation of China (51508505). The authors would like to thank the developers of R cpart package for DT algorithm, and Biogeme for NL models. The authors also appreciate Liang Tang from University of Maryland, who gave us suggestions on data processing. The authors are solely responsible for all statements in the paper.
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Zhu, Z., Chen, X., Xiong, C. et al. A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice. Transportation 45, 1499–1522 (2018). https://doi.org/10.1007/s11116-017-9770-6
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DOI: https://doi.org/10.1007/s11116-017-9770-6