Modeling time-of-day car use behavior: A Bayesian network approach |
| |
| Institution: | 1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Sipailou 2, Xuanwu District, Nanjing 210096, China;2. EcoTopia Science Institute & Green Mobility Collaborative Research Center, Nagoya University, Japan;3. Graduate School of Environmental Studies & Green Mobility Collaborative Research Center, Nagoya University, Japan;1. Departamento de Física, Universidade Federal de Sergipe/CCET, São Cristóvão, SE 49100-000, Brazil;2. Departamento de Química Fundamental, Universidade Federalde Pernambuco/CCEN, Cidade Universitária, Recife, PE 50670-901, Brazil;3. The Hebrew University of Jerusalem, Chemistry Institute, E. Safra Campus, 91904 Jerusalem, Israel;1. Département de physique, Université de Montréal, C.P. 6128 Succ. Centre-Ville, Montréal, QC H3C-3J7, Canada;2. Laboratoire AIM Paris-Saclay, CEA/DSM Université Paris-Diderot CNRS, IFRU/SAp, F-91191 Gif-sur-Yvette, France;3. European Centre for Medium-Range Weather Forecasts, Reading RG2 9AX, UK;1. Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Place Louis Pasteur, 3, B-1348 Louvain-la-Neuve, Belgium;2. Royal Observatory of Belgium, Ringlaan 3, B-1180 Brussels, Belgium;3. Institut für Geophysik und Meteorologie, Universität zu Köln, Albertus-Magnus-Platz, 50923 Köln, Germany |
| |
| Abstract: | 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. |
| |
| Keywords: | Car use Bayesian networks Latent class Machine learning GPS data |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
