Environmental screening model of driving behavior for an electric bus entering and leaving stops

Document Type

Journal Article

Publication Date


Subject Area

mode - bus, planning - methods, planning - environmental impact, technology - alternative fuels


Electric buses (E-Bus), energy consumption, driving behavior


Energy consumption determines the environmental benefits and driving range of electric vehicles. The driving behavior significantly affects vehicle energy consumption, but the implicit relationship between them is unclear. Natural driving data of Electric buses (E-Bus) on the BRT line were collected to evaluate the implicit relationship between driving behavior and energy consumption while entering and leaving bus stops. Statistical methods were used to analyze the driving characteristics and their influence on energy consumption. An environmental screening model of the driving behavior was established based on the extreme gradient boosting (XGBoost) algorithm to analyze the impact of the driving parameters on energy consumption and extract their implicit relationship. The results were compared with predictions obtained from random forest (RF) and support vector machine (SVM) models. The results showed that the prediction accuracy of the XGBoost algorithm was higher than that of the RF and SVM models for two-class (95.2%) and three-class (84.9%) classifications. Although the prediction accuracy was lower for the three-class model, its classification was more specific and accurately predicted the energy consumption level.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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