Estimation of Greenhouse Gas Emission Reduction from Shared Micromobility System

Shared micromobility is widely recognized as an environmentally friendly travel mode and a critical component of transportation decarbonization. However, quantitatively assessing its environmental impact using real-world trip data is an unresolved and challenging subject. In this research, we proposed a system combining machine learning algorithms and the Monte Carlo simulation to address this issue. First, several machine learning algorithms (Random Forest, XGBoost, and LightGBM) were utilized to identify citizens’ travel mode choice preferences and then estimate the substituted travel mode of each micromobility trip. Second, to ensure the reliability of the final environmental impact assessment, the Monte Carlo simulations were used to simulate the substituted mode of each trip. Then the environmental impacts were calculated based on the life cycle greenhouse gas emissions. Instead of estimating a specific number, we obtained a probabilistic outcome for environmental impacts by using the Monte Carlo simulation, which considers the uncertainty. According to the studies, the shared bike service and the shared e-scooter service have positive environmental impacts. However, these effects are limited compared to the transportation sector’s total emissions. The most compelling reason is that shared micromobility comprises a minuscule part of total urban travel.