2024-03-29T11:16:05Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00022973
2023-01-16T04:12:37Z
673:674:675
Modeling time-of-day car use behavior: A Bayesian network approach
Li, Dawei
Miwa, Tomio
Morikawa, Takayuki
open access
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Car use
Bayesian networks
Latent class
Machine learning
GPS data
In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.
Elsevier
2016-08
eng
journal article
AM
http://hdl.handle.net/2237/25154
https://nagoya.repo.nii.ac.jp/records/22973
https://doi.org/10.1016/j.trd.2016.04.011
1361-9209
Transportation Research Part D: Transport and Environment
47
54
66
https://nagoya.repo.nii.ac.jp/record/22973/files/Li_Miwa_Morikawa_partD2016.pdf
application/pdf
467.2 kB
2018-08-01