Summary of Estimating On-road Transportation Carbon Emissions From Open Data Of Road Network and Origin-destination Flow Data, by Jinwei Zeng and Yu Liu and Jingtao Ding and Jian Yuan and Yong Li
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data
by Jinwei Zeng, Yu Liu, Jingtao Ding, Jian Yuan, Yong Li
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hierarchical heterogeneous graph learning method, HENCE, leverages open data to estimate on-road carbon emissions with high accuracy. By incorporating origin-destination flow and road network data, HENCE constructs a multi-scale graph framework that models the connectivity between spatial areas. This approach outperforms baselines by 9.60% on average, demonstrating its effectiveness in estimating on-road transportation carbon emissions. The method’s superior performance is attributed to its ability to capture intrinsic interactions between travel demand and road network accessibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to estimate carbon emissions from vehicles using artificial intelligence. It uses data about where people go and how roads connect to make predictions about carbon emissions. This approach is better than others because it takes into account the relationships between different parts of the transportation system. The researchers tested their method on real-world data and found that it was very accurate, with a high degree of correlation. This could be useful for making policies to reduce carbon emissions. |
