title: Environmental impact of electric motorcycles: Evidence from traffic noise assessment by a building-based data mining technique.
abstract: This study provided new evidence on the potential adoption of electric motorcycle (EM) as a cleaner alternative to gasoline-powered motorcycle. The effects of EM on human exposure to traffic noise were assessed in different urban areas with different traffic scenarios. The assessment was carried out by a developed building-based model system that took into account the contribution of motorcycle traffic. The results indicated that the EM could be an appealing solution to reduce the risk of human exposure to excessive high traffic noise in a motorcycle city. Particularly, in a historical urban area in which the total traffic volume was lower and motorcycle traffic was dominant, the proportion of noise levels meeting the standard of 70 dB(A) increased significantly from 12.2% to 41.9% when 100% of gasoline motorcycles in the real traffic scenario were replaced by EMs. On the other hand, in a modern urban area in which the total traffic volume was higher and traffic noise levels at majority of sites were higher than 75 dB(A), the proportion of noise levels above 75 dB(A) decreased significantly from 82.6% to 59.9%. Nevertheless, the effect of EM on improving the traffic noise compliance rate in the modern urban area was not significant and other policies or measures need to be sought.
authors.affiliation: Department of Decision Sciences, Macau University of Science and Technology, Macao, China. Electronic address: nis@must.edu.mo.
authors.forename: N
authors.initials: N
authors.lastname: Sheng
authors.affiliation: Department of Decision Sciences, Macau University of Science and Technology, Macao, China.
authors.forename: X
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Department of Decision Sciences, Macau University of Science and Technology, Macao, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D057225:Data Mining; D004560:Electricity; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D009049:Motorcycles / Q000706:statistics & numerical data*; D009624:Noise, Transportation
keywords: Data-mining; Electric motorcycle; Risk assessment; Traffic noise