Interpretable and actionable vehicular greenhouse gas emission prediction at road link-level

To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of climate research. The appeal is that the predictive will inform policymakers, hopefully, in turn, they bring about systematic changes. Since transportation sector constantly among top contributors, especially populated urban areas, substantial effort has been going into building more informative to create sustainable environments. In this work, we seek establish framework emissions at road segment or link level networks. theme centres around model interpretability actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate DCM capable predicting link-level levels on networks parsimonious effective manner. Our results show up 85.4% accuracy models’ performances. also argue since goal most focuses involving make changes curb DCM-based suitable framework.