2023
Wu, Jishi; Jia, Peng; Feng, Tao; Li, Haijiang; Kuang, Haibo
Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications Journal Article
In: Transportation Research Part D: Transport and Environment, vol. 121, pp. 103839, 2023, ISSN: 1361-9209.
Abstract | Links | BibTeX | Tags: Built environment, GPBoost, Machine learning, Nonlinear effects, SHapley Additive ExPlanation, Traffic carbon emissions
@article{WU2023103839,
title = {Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications},
author = {Jishi Wu and Peng Jia and Tao Feng and Haijiang Li and Haibo Kuang},
url = {https://www.sciencedirect.com/science/article/pii/S1361920923002365},
doi = {https://doi.org/10.1016/j.trd.2023.103839},
issn = {1361-9209},
year = {2023},
date = {2023-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {121},
pages = {103839},
abstract = {Urban environmental policies need to be rectified considering the spatioemporal variations of traffic emissions. However, knowledge to support such a decision-making process is insufficient. This study analyzes the spatiotemporal distributions of traffic emissions in the built environment and their potential nonlinear associations. Considering the recent innovations in machine learning, a tree-boosting algorithm combined with Gaussian process and random effects models (GPBoost) is applied using the big GPS taxi data from Dalian, China. The nonlinear relationships between built environment variables and traffic carbon (CO2) emissions are interpreted using the SHapley Additive ExPlanation (SHAP). It is found that the proposed GPBoost model that considers spatial heterogeneity enhances the overall predictive power compared to traditional machine learning models. Most of the built environment variables have a nonlinear relationship with traffic carbon emissions and the threshold effects vary over time, indicating the necessity of dynamic urban management.},
keywords = {Built environment, GPBoost, Machine learning, Nonlinear effects, SHapley Additive ExPlanation, Traffic carbon emissions},
pubstate = {published},
tppubtype = {article}
}
Urban environmental policies need to be rectified considering the spatioemporal variations of traffic emissions. However, knowledge to support such a decision-making process is insufficient. This study analyzes the spatiotemporal distributions of traffic emissions in the built environment and their potential nonlinear associations. Considering the recent innovations in machine learning, a tree-boosting algorithm combined with Gaussian process and random effects models (GPBoost) is applied using the big GPS taxi data from Dalian, China. The nonlinear relationships between built environment variables and traffic carbon (CO2) emissions are interpreted using the SHapley Additive ExPlanation (SHAP). It is found that the proposed GPBoost model that considers spatial heterogeneity enhances the overall predictive power compared to traditional machine learning models. Most of the built environment variables have a nonlinear relationship with traffic carbon emissions and the threshold effects vary over time, indicating the necessity of dynamic urban management.