Abstract
Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.
Highlights
Fuel consumption of motor vehicles is one of the main sources of energy consumption in road transportation and has become one serious problem impacting the sustainable development of urban traffic system
Us, the current study aims at developing a practical model to precisely estimate the eco-level of individual driver performance during driving process. e database used in this study was the real-time data collected by on-board on-board diagnostics (OBD) + Global Position System (GPS) devices in real driving environments
In order to find a practical method to accurately estimate the eco-level of driver performance in naturalistic driving conditions, a back-propagation network based estimation model was developed in our current study
Summary
Fuel consumption of motor vehicles is one of the main sources of energy consumption in road transportation and has become one serious problem impacting the sustainable development of urban traffic system. E traditional estimation and predictive methods based on statistic models might not be suitable for accurately evaluating vehicle fuel consumption based on driver performance because of limited expressing capability for complicated relationships [24]. The predictive or identificative accuracy of these machine learning models was proved to be acceptable In addition to these estimation model construction methods of vehicle fuel consumption, the Internet and cloud computing technology have been changing and replacing traditional data sensing methods, which further accelerated the process of big data aggregation [30]. Us, the current study aims at developing a practical model to precisely estimate the eco-level of individual driver performance during driving process. In order to construct the estimation model to analyze the eco-level of driver performance and test its validation, 700 data samples were randomly selected for model trials and 100 data samples were used to test model accuracy
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