In:
Journal of Advanced Transportation, Hindawi Limited, Vol. 2021 ( 2021-9-14), p. 1-22
Abstract:
This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders.
Type of Medium:
Online Resource
ISSN:
2042-3195
,
0197-6729
DOI:
10.1155/2021/9219176
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2021
detail.hit.zdb_id:
2553327-7