Effect of Road, Environment, Driver, and Traffic Characteristics on Vehicle Emissions in Egypt

Ramadan, Ibrahim; Toukhy, Mahmoud El; Hussien, Khaled Z.; Tosti, Fabio; Shaaban, Ibrahim G.

doi:10.1007/s40999-022-00729-w

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…There are various factors affecting the generation of motor vehicle exhaust, such as the type of motor vehicle, the type of fuel, and the driving behavior of drivers. The latest traffic emission model MOVES ( Liu et al, 2019 ) will be divided into 13 types of motor vehicles, each type of motor vehicles, although in the same working conditions under the emission situation is not the same ( Hao et al, 2017 ; Ramadan et al, 2022 ); for the type of automobile fuel, there are a variety of types of fuel, the public generally known as gasoline, diesel fuel, and natural gas, but also some cars use ethanol, methanol, and liquid hydrogen and in recent years gradually developed electric power, of which, natural gas is the most popular type, the gas is the most common. Some cars use ethanol, methanol, and liquid hydrogen and gradually developed electric power in recent years, among which natural gas, methanol, ethanol, liquid hydrogen, etc.…”

Section: Introductionmentioning

confidence: 99%

Driving behavior characterization and traffic emission analysis considering the vehicle trajectory

Du,

Kang,

Gao

et al. 2024

Front. Psychol.

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Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver’s behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver’s driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver’s driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions.

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Section: Introductionmentioning

confidence: 99%