Fusion Modeling Method of Car-Following Characteristics
Car-following model is indispensable to evaluate the characteristics of car-following behaviors. Through an analysis and comparison of data-driven and theoretically driven car-following models, it shows that the data-driven model has poor interpretability and high quality of training data set is req...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8882217/ |
| _version_ | 1819169900906676224 |
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| author | Yufang Li Xiaoding Lu Chen Ren Hongwei Zhao |
| author_facet | Yufang Li Xiaoding Lu Chen Ren Hongwei Zhao |
| author_sort | Yufang Li |
| collection | DOAJ |
| description | Car-following model is indispensable to evaluate the characteristics of car-following behaviors. Through an analysis and comparison of data-driven and theoretically driven car-following models, it shows that the data-driven model has poor interpretability and high quality of training data set is required, while for the theoretical-driven model, it is unable to describe the individualized features and models of the driver so as to a low model accuracy. To solve the problem, a novel modelling method is proposed using adaptive Kalman filter algorithm to integrate the long-short-time memory neural network (LSTM) data-driven model with the IDM theoretical-driven model to build the car-following model. Test results of real driving data from a single driver prove that the fusion car-following model has higher accuracy than a single model, while at the same time highlighting the driver's personality compared to the IDM model. Besides, it improves the generalization ability of the traditional data model, which is reflected by better fitting in the extreme case (for example, the stable state when the acceleration, velocity is zero). Finally, the trajectory simulation results show that the proposed integrated data-driven car-following model can better simulate the micro-traffic behavior of car following. |
| first_indexed | 2024-12-22T19:26:51Z |
| format | Article |
| id | doaj.art-459f80ccc18b4f5c820199a79ba9bad6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:26:51Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-459f80ccc18b4f5c820199a79ba9bad62022-12-21T18:15:12ZengIEEEIEEE Access2169-35362019-01-01716277816278510.1109/ACCESS.2019.29493058882217Fusion Modeling Method of Car-Following CharacteristicsYufang Li0https://orcid.org/0000-0003-2307-0979Xiaoding Lu1Chen Ren2Hongwei Zhao3Department of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaDepartment of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaDepartment of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaFoton Motor Group, Beijing, ChinaCar-following model is indispensable to evaluate the characteristics of car-following behaviors. Through an analysis and comparison of data-driven and theoretically driven car-following models, it shows that the data-driven model has poor interpretability and high quality of training data set is required, while for the theoretical-driven model, it is unable to describe the individualized features and models of the driver so as to a low model accuracy. To solve the problem, a novel modelling method is proposed using adaptive Kalman filter algorithm to integrate the long-short-time memory neural network (LSTM) data-driven model with the IDM theoretical-driven model to build the car-following model. Test results of real driving data from a single driver prove that the fusion car-following model has higher accuracy than a single model, while at the same time highlighting the driver's personality compared to the IDM model. Besides, it improves the generalization ability of the traditional data model, which is reflected by better fitting in the extreme case (for example, the stable state when the acceleration, velocity is zero). Finally, the trajectory simulation results show that the proposed integrated data-driven car-following model can better simulate the micro-traffic behavior of car following.https://ieeexplore.ieee.org/document/8882217/Car-following characteristicLSTMIDMadaptive Kalman filterfusion modelling |
| spellingShingle | Yufang Li Xiaoding Lu Chen Ren Hongwei Zhao Fusion Modeling Method of Car-Following Characteristics IEEE Access Car-following characteristic LSTM IDM adaptive Kalman filter fusion modelling |
| title | Fusion Modeling Method of Car-Following Characteristics |
| title_full | Fusion Modeling Method of Car-Following Characteristics |
| title_fullStr | Fusion Modeling Method of Car-Following Characteristics |
| title_full_unstemmed | Fusion Modeling Method of Car-Following Characteristics |
| title_short | Fusion Modeling Method of Car-Following Characteristics |
| title_sort | fusion modeling method of car following characteristics |
| topic | Car-following characteristic LSTM IDM adaptive Kalman filter fusion modelling |
| url | https://ieeexplore.ieee.org/document/8882217/ |
| work_keys_str_mv | AT yufangli fusionmodelingmethodofcarfollowingcharacteristics AT xiaodinglu fusionmodelingmethodofcarfollowingcharacteristics AT chenren fusionmodelingmethodofcarfollowingcharacteristics AT hongweizhao fusionmodelingmethodofcarfollowingcharacteristics |