Back to Search Start Over

Research on Temporal and Spatial Distribution of Carbon Emissions from Urban Buses Based on Big Data Analysis.

Authors :
Long, Yan
Zhu, Changzheng
Zhang, Cong
Pan, Renjie
Source :
Atmosphere. Feb2023, Vol. 14 Issue 2, p411. 22p.
Publication Year :
2023

Abstract

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human's daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya's buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734433
Volume :
14
Issue :
2
Database :
Academic Search Index
Journal :
Atmosphere
Publication Type :
Academic Journal
Accession number :
162082804
Full Text :
https://doi.org/10.3390/atmos14020411