Coupling efficiency between bike-sharing demand and land-use: data envelopment analysis

Abstract

This study employs a hotspot areas-based data envelopment analysis (DEA) to investigate the coupling efficiency between bike-sharing demand and land use. Hotspot areas are used as decision-making units and resampling data is imposed spatial constraints. The elbow method is used to select the crucial indicators after exploring the correlation between coupling efficiency evaluation indexes using the gray correlation model. Since this coupling efficiency evaluation belongs to a “multi-input and single-output” system, DEA was introduced to build a quantitative model for bike-sharing demand & land-use coupling efficiency. This paper also identifies the factors that restrict bike-sharing demand generation. More importantly, different spatial scenarios are deployed to demonstrate heterogeneous characteristics of DEA efficiency in different locations. The results of the Beijing case show that DEA efficiency generally shows a trend from low to high from the inside out of the urban center. The land-use factors with relatively high redundancy rates are Financial Insurance Facilities, Hotels, and Living Facilities.