A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus

Hongli Liu, Weiguo Yun, Bin Li (), Mengling Dai and Yangyuhang Wang
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Hongli Liu: School of Automobile, Chang’an University, Xi’an 710064, China
Weiguo Yun: Zhejiang Geely Farizon New Energy Commercial Vehicles Group Co., Ltd., Hangzhou 311243, China
Bin Li: School of Automobile, Chang’an University, Xi’an 710064, China
Mengling Dai: Guizhou Xingqian Talent Resources Co., Ltd., Guiyang 550003, China
Yangyuhang Wang: School of Automobile, Chang’an University, Xi’an 710064, China

Sustainability, 2023, vol. 15, issue 10, 1-16

Abstract: In order to help improve the economy, energy savings and emission reductions of pure electric buses, based on the driving data, a new driving cycle construction method is proposed. Through the dividing of short trips and the calculation of characteristic parameter values, two typical driving conditions (weekday driving condition and weekend driving condition) are constructed via principal components analysis and the k-means clustering method, and both have a high degree of compatibility with the actual conditions. Based on the two typical driving conditions, the CRITIC (Criteria Importance Through Intercriteria Correlation) method and the quantitative analysis are used to establish a quantitative evaluation model to score the economy of the driver’s driving behavior. The result shows that the weekend working condition with the better traffic environment promotes the generation of aggressive driving behavior and increases the random fluctuation seen in the driver’s driving process: for the weekend driving condition, the proportion of low economic efficiency is about 4.5 times bigger than the proportion on weekdays, and the former’s fluctuation range for the driving behavior score is 37% higher than that of the latter, meaning that the overall economy of the pure electric bus is much worse on weekends.

Keywords: electric bus; driving cycle; driving behavior; data quantification; economy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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