Analysis of Alternative Fuel Vehicle (AFV) Adoption Utilizing Different Machine Learning Methods: A Case Study of 2017 NHTS

Abstract

Alternative fuel vehicles (AFVs) are considered as the one of policies towards the sustainable transportation with low fossil fuel consumption and greenhouse gas emission. However, the demand for AFV is still not clear, due to the complex interaction between influencing factors. This paper aimed to investigate the AFV adoption behavior in order to estimate the AFV penetration in specific regions. 2017 National Household Travel Survey (NHTS), providing the detailed information of AFV users, was utilized to conduct the factor analysis and establish the prediction model. Three groups of variables, including vehicle-related, household-related and individual-related variables, were considered. Additionally, the AFV users are only 4.2% of all the respondents, which is a typical imbalanced distribution. Thus, this paper also introduced the methods to deal with the imbalanced dataset for AFV users and conventional vehicle (CV) users, which was rarely investigated in existing studies. To construct the prediction model, five machine learning methods, Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT), were employed and compared. The performance analysis indicates that the RF model has the best prediction capability among the models. For validation, the RF prediction model was used to develop a AFV penetration map for U.S. by state. This study is believed to have a wide application in government policy making and vehicle manufacturing.