Emission Analysis of Electric Motorcycles and Assessment of Emission Reduction With Fleet Electrification
Journal
IEEE Transactions on Intelligent Transportation Systems
Date Issued
2023-01-01
Author(s)
Abstract
The motorcycle is a common mode of transport in many countries. Although there exist abundant studies on emissions from internal combustion engine motorcycles, little is known regarding the emissions from electric motorcycles (EMs). Methods commonly used in emissions analysis for internal combustion engine motorcycles are not suitable for non-exhaust emissions produced from EMs. Computer vision could provide the opportunity to facilitate analysis of EMs’ emissions analysis by utilizing EM counts in traffic videos captured from surveillance cameras. The proposed approach incorporates computer vision-based counting of EMs and internal combustion engine motorcycles in combination with ambient pollutant concentration monitoring. The measured pollutant concentration could be apportioned to EMs based on its proportion in the traffic flow. Results show that coarse particle number and PM $_{1.0}$ are associated to the number of EMs with P Values smaller than 0.001. We also assess the potential change in ambient pollutant concentration through sensitivity analysis on motorcycle fleet electrification. Replacing all internal combustion engine motorcycles with the EMs could potentially reduce urban pollutant emissions by 48.62% for fine particles, 23.54% for CO $_{2}$ , and 13.41% for CO. However, this leads to increase of 29.83% for coarse particles and 24.09% for PM $_{1.0}$ . Through collected real-world data, the contributions of this study are the utilization of EM counts in emission analysis and the quantification of emission reduction by motorcycle electrification. This study could be applied for exposure assessment to emissions and their related health impacts to commuters.
Subjects
Atmospheric measurements | deep learning | Electric two-wheeler | Feature extraction | Medical services | motorcycle classification | Motorcycles | non-exhaust emission | Particle measurements | Pollution measurement | Videos
Type
journal article