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. To this end, we proposed a system combining machine learning algorithms and 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. Finally, the environmental impacts were calculated based on the life cycle greenhouse gas emissions.
Estimation of Greenhouse Gas Emission Reduction from Shared Micromobility System. In: 2021 IEEE Green Energy and Smart Systems Conference (IGESSC), pp. 1-6, IEEE, Long Beach, CA, USA, 2021, ISSN: 2640-0138.
Estimation of Greenhouse Gas Emission Reduction from Shared Micromobility System