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Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data by Zeng, Jinwei; Liu, Yu; Ding, Jingtao; Yuan, Jian; Li, Yong is a scholarly article available to read on EtoBox.

What is Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data about?

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

Author

Zeng, Jinwei; Liu, Yu; Ding, Jingtao; Yuan, Jian; Li, Yong

Published

2024

Language

EN