High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data

Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on transaction data. The proposed firstly estimates transport mode substituted by each recorded trip utilizing route planning techniques online maps and well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions every are quantified using Life Cycle Analysis. is applied an empirical dataset from Shanghai, China. results reveal that substitution rates different modes substantial spatiotemporal variances depend strongly travel contexts, highlighting necessity analyzing impacts at level. Moreover, estimated save average 80.77 g CO2-eq GHG emissions versus than situations without Shanghai. annual reduced be 117 kt CO2-eq, which equals yearly over 25,000 typical gasoline passenger vehicles. Additionally, associations among built environments quantitatively investigated shed light spatial DLBS. can efficiently support benefit-cost analysis, planning, management