Citation

Du X, Kang X, Gao Y, Wang X. Front. Psychol. 2023; 14: e1341611.

Copyright

(Copyright © 2023, Frontiers Research Foundation)

DOI

10.3389/fpsyg.2023.1341611

PMID

38348110

PMCID

PMC10860677

Abstract

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.

Language: en

Keywords

driving behavior; K-means algorithm; MOVES; symbol approximation aggregation; traffic emissions