2022
Li, Xiaodong; Feng, Tao; Rasouli, Soora
Exploring random taste heterogeneity in choice modelling using mixture density network Conference
7th International Choice Modelling Conference (ICMC), May 23-25, 2022. Reykjavik, Iceland, 2022.
Abstract | Links | BibTeX | Tags: Choice models, Heterogeneity, Machine learning
@conference{8ee855dfda764e948993b1c62df890a4,
title = {Exploring random taste heterogeneity in choice modelling using mixture density network},
author = {Xiaodong Li and Tao Feng and Soora Rasouli},
url = {http://www.icmconference.org.uk/2022-icmc-reykjavik.html},
year = {2022},
date = {2022-01-31},
urldate = {2022-01-31},
address = {Reykjavik, Iceland},
organization = {7th International Choice Modelling Conference (ICMC), May 23-25, 2022.},
abstract = {Capturing heterogeneity in subjects’ decision making process, as accurate as possible, plays an essential role in choice modeling research. In this paper, we investigate the random taste heterogeneity in travel behavior modeling which is an integral part of decision-making process. In contrast to previous works, we use the Mixture Density Network (MDN) which is built from Neural Network and mixture Gaussian model to identify the latent heterogeneity. We assume that the taste variation of individuals follows a series of distribution with certain mean and standard deviation which are dependent on individual social demographic characteristics. We integrated this machine learning method into the discrete choice model and jointly estimated the parameters. Using the stated preference data of Swissmetro, we applied our proposed model and discovered random taste variations which are highly interpretable. We also compared the model with traditional mixed logit model and found the superiority of the proposed model.},
keywords = {Choice models, Heterogeneity, Machine learning},
pubstate = {published},
tppubtype = {conference}
}
Capturing heterogeneity in subjects’ decision making process, as accurate as possible, plays an essential role in choice modeling research. In this paper, we investigate the random taste heterogeneity in travel behavior modeling which is an integral part of decision-making process. In contrast to previous works, we use the Mixture Density Network (MDN) which is built from Neural Network and mixture Gaussian model to identify the latent heterogeneity. We assume that the taste variation of individuals follows a series of distribution with certain mean and standard deviation which are dependent on individual social demographic characteristics. We integrated this machine learning method into the discrete choice model and jointly estimated the parameters. Using the stated preference data of Swissmetro, we applied our proposed model and discovered random taste variations which are highly interpretable. We also compared the model with traditional mixed logit model and found the superiority of the proposed model.
2011
Feng, T; Arentze, T A; Timmermans, H J P
Assessing the relative importance of input variables for route choice modeling: a neural network approach Journal Article
In: Journal of the Eastern Asia Society for Transportation Studies, vol. 9, pp. 341-353, 2011, ISSN: 1881-1124.
Abstract | Links | BibTeX | Tags: Choice models, Machine learning
@article{Feng2011c,
title = {Assessing the relative importance of input variables for route choice modeling: a neural network approach},
author = {T Feng and T A Arentze and H J P Timmermans},
url = {https://www.jstage.jst.go.jp/article/easts/9/0/9_0_341/_article},
doi = {https://doi.org/10.11175/easts.9.341},
issn = {1881-1124},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
journal = {Journal of the Eastern Asia Society for Transportation Studies},
volume = {9},
pages = {341-353},
abstract = {This paper identifies the relative importance of variables influencing route choice using a neural network approach. Variables related to route attributes and choice contexts are simultaneously incorporated into the model, and a weight partition algorithm is employed to calculate the strength of influence on route choice decisions. The network is trained and validated using stated preference data. Simulation results show good predictability (97.4% of accuracy) of the neural network model. The relative importance of input variables indicates that road category, pricing, bonus and passing through an urban area are more important. Among all choice contexts, the size of truck is most important, followed by travel time difference and road length. The relative importance identified by the neural network model is consistent with the results of a multinomial logit model, and provide meaningful references for variable selection and model estimation.},
keywords = {Choice models, Machine learning},
pubstate = {published},
tppubtype = {article}
}
This paper identifies the relative importance of variables influencing route choice using a neural network approach. Variables related to route attributes and choice contexts are simultaneously incorporated into the model, and a weight partition algorithm is employed to calculate the strength of influence on route choice decisions. The network is trained and validated using stated preference data. Simulation results show good predictability (97.4% of accuracy) of the neural network model. The relative importance of input variables indicates that road category, pricing, bonus and passing through an urban area are more important. Among all choice contexts, the size of truck is most important, followed by travel time difference and road length. The relative importance identified by the neural network model is consistent with the results of a multinomial logit model, and provide meaningful references for variable selection and model estimation.