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Despite the fuel use and emission benefits of hybrid electric vehicles (HEVs), few studies have characterized in detail emission patterns and driving volatility profiles from HEVs in different road types under real-world driving conditions. This article examined the relationship between hot-stabilized emissions, engine variables, internal combustion engine status, roadway characteristics, and vehicular jerk types. Data were collected from a Toyota HEV subcompact on a driving route over rural, urban, and highway roads in the Aveiro region (Portugal). Driving volatility was represented by six driving styles based on combinations of acceleration/deceleration and vehicular jerk. Clustering and Disjoint Principal Component Analysis (CDPCA) was applied to extract principal components and identify specific clusters among variables. Both route type and internal combustion engine (ICE) operating behavior showed to have an impact on the distribution of vehicular jerk types. The proposed CDPCA constrained to the road sector exhibited different shapes in the clusters of the jerk types between ICE operation status. This article can provide insights into emissions monitoring analysis of the new generation of HEVs about the description of volatile driving behaviors. [ABSTRACT FROM AUTHOR]

This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and speed), supporting or replacing noise sensor networks, and addressing noise pollution concerns. RTNM is composed of two parts: i) the Vehicle Noise Specific Power model (for the assessment of the sound power level – L w of passenger cars, considering speed and motorization information) coupled with the CNOSSOS model (for the estimation of heavy-duty vehicles' L w); and ii) a sound propagation model (to evaluate noise levels at the receiver point). Inputs for the RTNM are retrieved from a radar installed in Aveiro city, Portugal, while the model estimations are validated by comparing them with levels recorded by a noise sensor installed close to the radar. It was found that the resulting noise estimations are robust, with associated mean absolute percentage errors not exceeding 5.8%. [ABSTRACT FROM AUTHOR]