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Updated: May 10, 2025

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
Published on: June 12, 2016
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.
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.
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