Development of a Practical Method to Estimate the Eco-Level of Driver Performance
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8151720 |
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| _version_ | 1849691073401585664 |
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| author | Jianbin Zheng Yiping Wu |
| author_facet | Jianbin Zheng Yiping Wu |
| author_sort | Jianbin Zheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e8b75a8343a94a6fa0f4d163f62060d6 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e8b75a8343a94a6fa0f4d163f62060d62025-08-20T03:21:09ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/81517208151720Development of a Practical Method to Estimate the Eco-Level of Driver PerformanceJianbin Zheng0Yiping Wu1Faculty of Information Technology, Beijing University of Technology, No. 100 Ping Le Yuan, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, No. 100 Ping Le Yuan, Beijing 100124, ChinaMotor 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.http://dx.doi.org/10.1155/2020/8151720 |
| spellingShingle | Jianbin Zheng Yiping Wu Development of a Practical Method to Estimate the Eco-Level of Driver Performance Journal of Advanced Transportation |
| title | Development of a Practical Method to Estimate the Eco-Level of Driver Performance |
| title_full | Development of a Practical Method to Estimate the Eco-Level of Driver Performance |
| title_fullStr | Development of a Practical Method to Estimate the Eco-Level of Driver Performance |
| title_full_unstemmed | Development of a Practical Method to Estimate the Eco-Level of Driver Performance |
| title_short | Development of a Practical Method to Estimate the Eco-Level of Driver Performance |
| title_sort | development of a practical method to estimate the eco level of driver performance |
| url | http://dx.doi.org/10.1155/2020/8151720 |
| work_keys_str_mv | AT jianbinzheng developmentofapracticalmethodtoestimatetheecolevelofdriverperformance AT yipingwu developmentofapracticalmethodtoestimatetheecolevelofdriverperformance |