用探针车数据建模城市交通碳排放，实现区域级实时监测与优化。
Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach
- 基于探针车数据，用机器学习预测大尺度城市交通与排放关系。
- 首次构建美国城市区域的宏观排放基本图（eMFD），揭示排放的空间依赖性。
- 为交通管理者提供碳排放量化工具，支持因地制宜的减排决策。
城市拥堵导致车辆低效运行，加剧温室气体排放与空气污染。宏观排放基本图（eMFD）刻画了网络层面排放与交通总量之间的有序关系，可实现区域排放的实时监测，并优化交通需求分配以缓解拥堵及排放。然而，受历史数据限制，实证驱动的eMFD模型稀疏。本研究利用大规模、高粒度的探针车交通与排放数据，首次在美大型城市范围内应用机器学习方法，预测交通流量与网络整体排放率的关系。分析框架与发现生成了数据驱动的eMFD，深化了对排放受网络结构、基础设施、土地利用及车辆特征影响的空间依赖性的理解，使交通管理部门能够根据给定出行需求估算城市交通碳排放，并优化局部交通管理与规划策略，实现全局减排。
原文摘要 · Abstract (English)
Urban congestions cause inefficient movement of vehicles and exacerbate greenhouse gas emissions and urban air pollution. Macroscopic emission fundamental diagram (eMFD)captures an orderly relationship among emission and aggregated traffic variables at the network level, allowing for real-time monitoring of region-wide emissions and optimal allocation of travel demand to existing networks, reducing urban congestion and associated emissions. However, empirically derived eMFD models are sparse due to historical data limitation. Leveraging a large-scale and granular traffic and emission data derived from probe vehicles, this study is the first to apply machine learning methods to predict the network wide emission rate to traffic relationship in U.S. urban areas at a large scale. The analysis framework and insights developed in this work generate data-driven eMFDs and a deeper understanding of their location dependence on network, infrastructure, land use, and vehicle characteristics, enabling transportation authorities to measure carbon emissions from urban transport of given travel demand and optimize location specific traffic management and planning decisions to mitigate network-wide emissions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。