Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Isabella Yunfei Zeng, Shiqi Tan, Jianliang Xiong, Xuesong Ding, Yawen Li and Tian Wu
Additional contact information
Isabella Yunfei Zeng: UK-China (Guangdong) CCUS Centre, Guangzhou 510663, China
Shiqi Tan: Department of Automation, Tsinghua University, Beijing 100084, China
Jianliang Xiong: School of Economics and Management, Tsinghua University, Beijing 100084, China
Xuesong Ding: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yawen Li: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Tian Wu: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2021, vol. 14, issue 23, 1-19

Abstract: Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R 2 ) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

Keywords: real-world fuel consumption rate; machine learning; big data; light-duty vehicle; China (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/23/7915/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/23/7915/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:23:p:7915-:d:687690

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().