Spatiotemporal characteristics of green travel: A classification study on a public bicycle system

Abstract Understanding the characteristics of users and stations provides the foundation for a more efficient public bicycle system. Based on the real-time data of the Nanjing public bicycle system, we presented the spatiotemporal characteristics of users and stations combining data mining and geographic visualization. First, we analyzed users' gender, age, weekly flow, and time-segment flow, and classified the users into different types. In addition, we studied the cycling chains of certain users in details to understand the differences. Second, we analyzed the station distribution, station flow, station time-segment flow, and the surrounding environment, and studied the specific stations of different types to reveal the diverse characteristics. Moreover, we also explored the relationship between the user types and the station types. The results showed that public bicycles were mainly used for commuting or transferring, and social and economic activities around stations greatly influenced the use of public bicycles. However, the usage of the public bicycle system was still at a low level. Furthermore, different types of users had different cycling purposes, and different types of stations showed different characteristics of renting flow and returning flow. At last, we proposed different incentives and management measures for different types of users and stations.