Assessing economic efficiency of driving behavior within traffic flow dynamics

The widespread adoption of electric vehicles has a positive impact on energy conservation and emission reduction, significantly contributing to sustainable development. This paper proposes an economic evaluation method for drivers’ driving behaviors based on actual operational data of pure electric vehicles. This method aims to eliminate the influence of traffic conditions on the evaluation results, ensuring that the outcomes solely reflect the economic efficiency level of drivers’ behaviors. Initially, short trips are identified and traffic conditions are classified and identified based on real vehicle data. A random forest identification model is established to recognize the traffic conditions of short trips in real-time. Subsequently, characteristic variables are selected and calculated. Pearson correlation analysis wand Spearman correlation analysis are utilized to identify variables strongly correlated with energy consumption and traffic conditions, which are then used as evaluation indicators. Finally, eco-scores are calculated using the energy consumption per unit mileage for short trip segments under different traffic conditions. A neural network model is established and trained between the evaluation indicators and eco-scores, enabling real-time evaluation of the economic efficiency of drivers’ driving behaviors. The evaluation method proposed in this paper can objectively assess the economic efficiency of driving behavior, facilitating drivers to improve their driving practices based on their evaluation results.