Multidimensional Visualization of Bikeshare Travel Patterns Using a Visual Data Mining Technique: Data Cubes

In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users, an online analytical processing (OLAP) tool called data cube was used for treating and displaying multi-dimensional data. We extended and modified the traditionally three-dimensional data cube into four dimensions, which are space, date, time, and user, each with a user-specified hierarchy, and took transaction numbers and travel time as two quantitative measures. The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays, while the volume at weekends has an approximate even distribution. Bad weather condition significantly restricts the bikeshare usage. Besides, seamless smartcard users generally take a longer trip than exclusive smartcard users; and non-native users ride faster than native users. These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data, but also raise equity concerns among bikeshare users with different demographic backgrounds.