Examining spatio-temporal trip patterns of shared bicycle transferred to metro: a case study of Xi’an City, China

Abstract

Bike-sharing not only provides more options for urban transportation trips but also has an important impact on the transportation system. Bike sharing plays an important role in making up for other public transport. Studies have shown that bike sharing expands the coverage of subway stations. In this paper, a time series clustering algorithm based on the K-means algorithm and DTW distance is proposed to cluster the time series of shared bicycles that transfer to subway stations. The shared bicycles transferred to subway stations are identified by building a buffer zone at the entrance and exit of a subway station. The results show that the temporal patterns of bike-sharing in different metro stations can be classified into five major categories. The temporal patterns of bicycle sharing are related to the land use characteristics near the metro stations, and for residential and commercial metro stations, the trips are more and the peak duration is longer. The travel volume is decreasing from the city center to the surrounding area. The spatio-temporal patterns of the transferred shared bicycle can provide feasible suggestions for the scheduling and allocation of shared bicycles, and provide help for optimizing urban transportation.