Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Helinyi Peng, Yuuki Nishiyama, Kaoru Sezaki
Assessing environmental benefits from shared micromobility systems using machine learning algorithms and Monte Carlo simulation Journal Article Open Access
In: Sustainable Cities and Society, pp. 104207, 2022, ISSN: 2210-6707.
Abstract | BibTeX | タグ: Big data, environmental impacts, Greenhouse gas (GHG) emission, Machine learning, shared micromobility | Links:
@article{PENG2022104207,
title = {Assessing environmental benefits from shared micromobility systems using machine learning algorithms and Monte Carlo simulation},
author = {Helinyi Peng and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://www.sciencedirect.com/science/article/pii/S2210670722005157},
doi = {https://doi.org/10.1016/j.scs.2022.104207},
issn = {2210-6707},
year = {2022},
date = {2022-10-01},
urldate = {2022-01-01},
journal = {Sustainable Cities and Society},
pages = {104207},
abstract = {Shared micromobility systems (SMSs) are paving the way for new, more convenient travel options while also lowering transportation-related greenhouse gas (GHG) emissions. However, few studies have used real-world trip data to estimate SMSs' environmental benefits, especially for dockless scooter-sharing services. To this end, we proposed a system to estimate the GHG emission reduction effected by SMSs. First, several machine learning (ML) algorithms were utilized to identify citizens' travel mode choice preferences, and then the mode substituted by each shared micromobility trip was estimated. We compared the ML algorithms' estimation results and selected those from the random forest, lightGBM, and XGBoost model for further estimating GHG reductions. Second, the Monte Carlo simulations were used to simulate the substituted mode at the trip level to improve the reliability of the final GHG reduction estimation. Finally, the environmental benefits were calculated based on the trip distances and the travel modes that were substituted. Instead of estimating a specific number, we obtained a probabilistic outcome for the environmental benefits while considering the level of uncertainty. Our results suggest that SMSs have positive environmental impacts and have the potential to facilitate the decarbonization of urban transport. According to these findings, implications and suggestions on extending SMSs' environmental benefits are proposed.},
keywords = {Big data, environmental impacts, Greenhouse gas (GHG) emission, Machine learning, shared micromobility},
pubstate = {published},
tppubtype = {article}
}