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This study provides an approach to identify driving style of truck drivers by using GPS, load condition, and in-vehicle monitoring data and investigates the association of driving styles with risky driving behaviors from macro and micro perspectives. The naturalistic driving data used in this study were collected from 4,357 trucks in Hangzhou, China over three months in 2021. Six driving volatility parameters and six warning parameters were used to characterize the driving styles. Then, three driving styles under the two load conditions were identified using k-means clustering methods and principal component analysis. Finally, one-way MANOVA and ANOVA were used to analyze the relationship between driving styles and driving risk. It was found that truck drivers have different thresholds for aggressive and cautious driving style under different load conditions. Truck drivers who exhibited aggressive driving under both load conditions exhibited high driving risk. Although most truck drivers exhibited safe or normal driving under both conditions, the few who exhibited aggressive driving contribute to a disproportionate driving risk. These results can help distinguish differences in truck drivers' driving styles under different load conditions, thus providing a more comprehensive safety assessment of truck drivers' performance for monitoring purposes. [ABSTRACT FROM AUTHOR]

In this work, two types of piezoelectric structures A and B are designed for harvesting maximum amount of energy from motor vibrations. The analysis is conducted under various load conditions with the placement of two different shapes of proof mass (square and rectangular) on the piezoelectric energy harvester. An experimental study is conducted for harvesting energy from motor vibrations using two piezoelectric structures A and B, in which, structure B with the placement of square-proof mass generates maximum power, which is efficient for powering low-power electronic applications. [ABSTRACT FROM AUTHOR]