论文标题
路线级别的可解释且可操作的车辆温室气体排放预测
Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level
论文作者
论文摘要
为了有助于系统地降低人为的温室气体（GHG）排放，准确而精确的温室气体发射预测模型已成为气候研究的重点。呼吁是，预测模型将为决策者提供通知，并希望反过来，他们将带来系统的变化。由于运输部门一直是温室气排放的最大贡献者，尤其是在人口稠密的城市地区，因此在建立更准确和信息丰富的温室气预测模型方面一直在努力，以帮助创造更可持续的城市环境。在这项工作中，我们试图在城市路段或运输网络链接水平上建立温室气体排放的预测框架。该框架的关键主题围绕使用计量经济学选择建模（DCM）的高级决策者的模型可解释性和可行性。我们说明，DCM能够以一种简约有效的方式预测城市道路网络上的连接级温室气体排放水平。在DCM模型的性能中，我们的结果表现出高达85.4％的预测准确性。我们还认为，由于大多数GHG排放预测模型的目标都集中在涉及高级决策者进行更改和遏制排放量上，因此基于DCM的GHG GHG排放预测框架是最合适的框架。
To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, especially in populated urban areas, substantial effort has been going into building more accurate and informative GHG prediction models to help create more sustainable urban environments. In this work, we seek to establish a predictive framework of GHG emissions at the urban road segment or link level of transportation networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner. Our results show up to 85.4% prediction accuracy in the DCM models' performances. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework.
