Accounting for over 20% of the total carbon emissions, the precise estimation
of on-road transportation carbon emissions is crucial for carbon emission
monitoring and efficient mitigation policy formulation. However, existing
estimation methods typically depend on hard-to-collect individual statistics of
vehicle miles traveled to calculate emissions, thereby suffering from high data
collection difficulty. To relieve this issue by utilizing the strong pattern
recognition of artificial intelligence, we incorporate two sources of open data
representative of the transportation demand and capacity factors, the
origin-destination (OD) flow data and the road network data, to build a
hierarchical heterogeneous graph learning method for on-road carbon emission
estimation (HENCE). Specifically, a hierarchical graph consisting of the road
network level, community level, and region level is constructed to model the
multi-scale road network-based connectivity and travel connection between
spatial areas. Heterogeneous graphs consisting of OD links and spatial links
are further built at both the community level and region level to capture the
intrinsic interactions between travel demand and road network accessibility.
Extensive experiments on two large-scale real-world datasets demonstrate
HENCE's effectiveness and superiority with R-squared exceeding 0.75 and
outperforming baselines by 9.60% on average, validating its success in
pioneering the use of artificial intelligence to empower carbon emission
management and sustainability development. The implementation codes are
available at this link: this https URL

利用人工智能的强大模式识别能力，通过整合代表交通需求和路网数据的开放数据源构建了一种用于公路碳排放估计的层次异构图学习方法 (HENCE)，在两个大规模真实世界数据集上的广泛实验验证了该方法的有效性和优越性，验证了其在推动人工智能应用于碳排放管理和可持续发展方面的成功。