Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach

Abstract

The ownership of electric bikes in China has increased dramatically in the past decades. Because of the strict policy in order to regulate the electric bikes, most of the owners do not register their license plates, which results in difficulties in the demand prediction and management of this kind of vehicle. This study aimed at investigating the factors that influences the registration of electric bikes in China and finding some potential reasons. A survey was conducted in Nanjing, China, and among the 844 electric bike users that were interviewed, 56% have not registered their plates. Based on the data, a Bayesian binary logit (BBL) model was built to evaluate how the various factors affected the registration of electric bikes. The results showed that registration was affected by factors including gender, age, education level, holding automobile driver license, household electric bike ownership, household car ownership, household income, and frequency of travel by electric bike. The study also investigated the reasons why electric bike users did not register license plates. A Bayesian Multinomial logit (BMNL) model was built to evaluate the reason of each choice taken by electric bike users. The study can help to understand better the behavior and psychology of electric bike registration. Based on the results, some suggestions were discussed in order to increase the electric bike registration rate in Chinese cities.