Insights into transportation CO2 emissions with big data and artificial intelligence
Zhenyu Luo, Tingkun He, Zhaofeng Lv, Junchao Zhao, Zhining Zhang, Yongyue Wang, Wen Yi, Shiyu Lu, Kebin He, Huan Liu
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.