Journal
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
Volume 47, Issue -, Pages 54-66Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trd.2016.04.011
Keywords
Car use; Bayesian networks; Latent class; Machine learning; GPS data
Funding
- Projects of International Cooperation and Exchange of the National Natural Science Foundation of China [5151101143]
- Science and technology Project of Jiangsu Province, China [BK20150613]
- Fundamental Research Funds for the Central Universities
- EPSRC [EP/N010612/1] Funding Source: UKRI
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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. (C) 2016 Elsevier Ltd. All rights reserved.
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