Insights into transportation CO<sub>2</sub> emissions with big data and artificial intelligence.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 40264962.
- Also identified by DOI 10.1016/j.patter.2025.101186 and PMC identifier 12010448.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.