Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data
To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. F...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/9/1208 |
| _version_ | 1797488726772809728 |
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| author | Qiqin Cai Dingrong Yi Fumin Zou Zhaoyi Zhou Nan Li Feng Guo |
| author_facet | Qiqin Cai Dingrong Yi Fumin Zou Zhaoyi Zhou Nan Li Feng Guo |
| author_sort | Qiqin Cai |
| collection | DOAJ |
| description | To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (<i>MAE</i>s) of dwell time are 52 and 14 s, respectively, and the root mean square errors (<i>RMSE</i>s) are 69 and 22 s, respectively. In addition, the confidence level of controlling the <i>MAE</i> of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA. |
| first_indexed | 2024-03-10T00:06:28Z |
| format | Article |
| id | doaj.art-9ca63c381fec4637af6ba17e92bdeb39 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T00:06:28Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9ca63c381fec4637af6ba17e92bdeb392023-11-23T16:07:48ZengMDPI AGEntropy1099-43002022-08-01249120810.3390/e24091208Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC DataQiqin Cai0Dingrong Yi1Fumin Zou2Zhaoyi Zhou3Nan Li4Feng Guo5School of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaSchool of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, ChinaFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, ChinaDigital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou 350118, ChinaDigital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaTo scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (<i>MAE</i>s) of dwell time are 52 and 14 s, respectively, and the root mean square errors (<i>RMSE</i>s) are 69 and 22 s, respectively. In addition, the confidence level of controlling the <i>MAE</i> of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA.https://www.mdpi.com/1099-4300/24/9/1208VR-XGBoostK-VDTEETC dataESAsdata mining |
| spellingShingle | Qiqin Cai Dingrong Yi Fumin Zou Zhaoyi Zhou Nan Li Feng Guo Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data Entropy VR-XGBoost K-VDTE ETC data ESAs data mining |
| title | Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data |
| title_full | Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data |
| title_fullStr | Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data |
| title_full_unstemmed | Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data |
| title_short | Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data |
| title_sort | recognition of vehicles entering expressway service areas and estimation of dwell time using etc data |
| topic | VR-XGBoost K-VDTE ETC data ESAs data mining |
| url | https://www.mdpi.com/1099-4300/24/9/1208 |
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