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Insights into transportation CO2 emissions with big data and artificial intelligence
Zhenyu Luo1, Tingkun He1, Zhaofeng Lv1
1State Key Joint Laboratory of ESPC, School of Environment, Tsinghua University, Beijing 100084, China.
Patterns (New York, N.Y.)
|April 23, 2025
Summary
Big data in transportation can aid decarbonization efforts. Artificial intelligence, including machine learning and deep learning, helps analyze complex data for insights into carbon dioxide emissions.
Area of Science:
- Transportation science
- Environmental science
- Data science
Background:
- The transportation sector faces challenges in reducing carbon dioxide emissions.
- Big data presents opportunities for deep decarbonization but is complex and voluminous.
- Extracting actionable insights from transportation big data is difficult.
Purpose of the Study:
- To discuss the application of transportation big data for understanding carbon dioxide emissions.
- To introduce artificial intelligence (AI) models for data assimilation and interpretation.
- To suggest methods for leveraging machine learning (ML) and deep learning (DL) for data analysis.
Main Methods:
- Reviewing the use of big data in the transportation sector.
- Introducing artificial intelligence models like machine learning and deep learning.
- Proposing ML for low-dimensional data interpretation and DL for spatiotemporal data predictability.
Main Results:
- AI models can effectively assimilate and interpret complex transportation big data.
- Machine learning is suitable for analyzing lower-dimensional datasets.
- Deep learning excels at enhancing the predictability of data with spatial connections over multiple timescales.
Conclusions:
- Interdisciplinary collaboration is crucial for overcoming challenges in algorithms, data, and computation.
- AI offers powerful tools for utilizing big data to achieve transportation decarbonization.
- Effective data interpretation and model application are key to understanding and reducing emissions.
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