An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment
Accurately acquiring the ecolevel of individual driver performance is the precondition for more targeted ecodriving behavior optimization. Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and v...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/9058674 |
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| _version_ | 1832559903644844032 |
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| author | Yiping Wu Xiaohua Zhao Ying Yao Jian Rong |
| author_facet | Yiping Wu Xiaohua Zhao Ying Yao Jian Rong |
| author_sort | Yiping Wu |
| collection | DOAJ |
| description | Accurately acquiring the ecolevel of individual driver performance is the precondition for more targeted ecodriving behavior optimization. Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. Based on driving simulator tests, data of driver performance and vehicle fuel consumption were collected. The ecolevel was indicated as the ecoscore corresponding to vehicle fuel consumption. The model input was designed as 10 feature indexes of driver performance (e.g., percentage number, mean value, standard deviation, and power of applying acceleration pedal). The output was treated as ecoscore. Taking a number of one hundred of data segments in vehicle starting process as training sample, the optimal structure, functions, and learning rate of a backpropagation neural network model with three layers were obtained, after repeated model simulation experiments. The validation test of 16 sample data items showed that the mean prediction accuracy of our developed model was 92.89%. In addition, comparative analysis displayed that the performance of backpropagation neural network based model was better than linear regression based model and random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the ecolevel of driver performance. The study results provide an effective method to grasp the ecolevel of driver performance and further contribute to driving behavior optimization towards vehicle fuel consumption and emissions reduction. |
| format | Article |
| id | doaj-art-73d149367bee4d7a880ee7e9724e07f9 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-73d149367bee4d7a880ee7e9724e07f92025-02-03T01:28:56ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/90586749058674An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator ExperimentYiping Wu0Xiaohua Zhao1Ying Yao2Jian Rong3Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, 100 Ping Le Yuan, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, 100 Ping Le Yuan, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, 100 Ping Le Yuan, Beijing 100124, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, 100 Ping Le Yuan, Beijing 100124, ChinaAccurately acquiring the ecolevel of individual driver performance is the precondition for more targeted ecodriving behavior optimization. Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. Based on driving simulator tests, data of driver performance and vehicle fuel consumption were collected. The ecolevel was indicated as the ecoscore corresponding to vehicle fuel consumption. The model input was designed as 10 feature indexes of driver performance (e.g., percentage number, mean value, standard deviation, and power of applying acceleration pedal). The output was treated as ecoscore. Taking a number of one hundred of data segments in vehicle starting process as training sample, the optimal structure, functions, and learning rate of a backpropagation neural network model with three layers were obtained, after repeated model simulation experiments. The validation test of 16 sample data items showed that the mean prediction accuracy of our developed model was 92.89%. In addition, comparative analysis displayed that the performance of backpropagation neural network based model was better than linear regression based model and random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the ecolevel of driver performance. The study results provide an effective method to grasp the ecolevel of driver performance and further contribute to driving behavior optimization towards vehicle fuel consumption and emissions reduction.http://dx.doi.org/10.1155/2018/9058674 |
| spellingShingle | Yiping Wu Xiaohua Zhao Ying Yao Jian Rong An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment Journal of Advanced Transportation |
| title | An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment |
| title_full | An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment |
| title_fullStr | An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment |
| title_full_unstemmed | An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment |
| title_short | An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment |
| title_sort | ecolevel estimation method of individual driver performance based on driving simulator experiment |
| url | http://dx.doi.org/10.1155/2018/9058674 |
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