How does travel satisfaction affect preference for shared electric vehicles? An empirical study using large-scale monitoring data and online text mining

Abstract

Shared electric vehicles (SEVs) are an emerging mode of transportation that offers advantages in environmental protection. It is unclear what role travel satisfaction plays in operations. User's feedback is an important factor to the future development of SEVs. How to accurately collect user's feedback and predictions is the focus of attention. In this paper, we innovatively used sentiment analysis to construct the travel satisfaction index based on text data from the largest social network platforms in China. In addition, we used a vector autoregression model to analyse and confirm that users' trip satisfaction is an important factor influencing subsequent use of SEVs. A large-scale data set of travel records combined with point of interest information of SEVs that covered 1.64 million records of 3,100 vehicles. The results showed that low rates of satisfaction of SEVs are attributed to the fault rate of vehicles and poor services. Arranging SEV services around restaurants and commercial areas will result in higher user's satisfaction. Moreover, an increase in user's satisfaction will increase the usage frequency of SEVs in future trips. An increase in satisfaction will reduce rental times of returning users in the short term, but it has no effect on travel distance.