Publicação
Biplots of kinematic variables and pollutant emissions for an intercity corridor
| Resumo: | A thorough understanding of driver behavior is an important step to improve the environmental performance of road traffic. Accurate data analysis tools can be valuable to identify these concerns. The present study explores relationships between driving patterns, tailpipe emissions, and road differentiation by using Principal Component Analysis Biplot. This statistical methodology is suitable to identify patterns hidden in data and can be used as a visualization tool. In this study, the key variables included were: speed, engine speed (RPM), acceleration, and vehicular jerk (first derivative of acceleration), as kinematic variables, and carbon dioxide (CO2), nitrogen oxides (NOx), and VSP (Vehicle Specific Power) mode, as pollutant emission variables. For this purpose, second-by-second vehicle dynamics and tailpipe emissions data were collected in three passenger cars with different powertrains (gasoline, diesel, and hybrid) along with different types of routes (one partly urban/rural and two motorways with variations in traffic volumes) in Aveiro Region (Portugal). Results revealed that Biplots allowed to distinguish different driving behaviors, separate route types (urban/rural from motorways), establish some remarks about emissions, and also present the correlated variables in a single plot. Therefore, this technique can be considered as a useful visualization tool to explore real traffic-related data. |
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| Autores principais: | Ferreira, Elisabete |
| Outros Autores: | Macedo, Eloísa; Fernandes, Paulo; Bahmankhah, Behnam; Coelho, Margarida C. |
| Assunto: | Biplots Principal component analysis Tailpipe emissions Kinematic variables |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | A thorough understanding of driver behavior is an important step to improve the environmental performance of road traffic. Accurate data analysis tools can be valuable to identify these concerns. The present study explores relationships between driving patterns, tailpipe emissions, and road differentiation by using Principal Component Analysis Biplot. This statistical methodology is suitable to identify patterns hidden in data and can be used as a visualization tool. In this study, the key variables included were: speed, engine speed (RPM), acceleration, and vehicular jerk (first derivative of acceleration), as kinematic variables, and carbon dioxide (CO2), nitrogen oxides (NOx), and VSP (Vehicle Specific Power) mode, as pollutant emission variables. For this purpose, second-by-second vehicle dynamics and tailpipe emissions data were collected in three passenger cars with different powertrains (gasoline, diesel, and hybrid) along with different types of routes (one partly urban/rural and two motorways with variations in traffic volumes) in Aveiro Region (Portugal). Results revealed that Biplots allowed to distinguish different driving behaviors, separate route types (urban/rural from motorways), establish some remarks about emissions, and also present the correlated variables in a single plot. Therefore, this technique can be considered as a useful visualization tool to explore real traffic-related data. |
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