Car sharing system: what transaction datasets reveal on users' behaviors

Abstract

Car sharing systems are gaining new members every month. However, few researches are conducted to better understand how these systems are used. In this paper, typical patterns of use of the car sharing system are identified using a transaction database covering a full year of operation. Data mining techniques are used to classify users according to their temporal patterns of car use frequency, traveled distance, and week use variability. The experiments reveal various classes of users. With respect to number of transactions throughout the year, users are segmented in two large classes: the regular and occasional ones, the majority of users belonging to the latter. The study of average trip length leads to the identification of 5 clusters of users. Finally, 8 types of typical weeks of use are described. Information about users' patterns could help the car sharing managers to optimize the use of the cars. It can also assist users in selecting the most advantageous subscription offer.