Study on Fractal Multistep Forecast for the Prediction of Driving Behavior
The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/9150583 |
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| _version_ | 1850215958385262592 |
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| author | Longhai Yang Hong Xu Xiqiao Zhang Shuai Li Wenchao Ji |
| author_facet | Longhai Yang Hong Xu Xiqiao Zhang Shuai Li Wenchao Ji |
| author_sort | Longhai Yang |
| collection | DOAJ |
| description | The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed. |
| format | Article |
| id | doaj-art-81816efba7b649c5afb4fbb679d0b9e9 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-81816efba7b649c5afb4fbb679d0b9e92025-08-20T02:08:27ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/91505839150583Study on Fractal Multistep Forecast for the Prediction of Driving BehaviorLonghai Yang0Hong Xu1Xiqiao Zhang2Shuai Li3Wenchao Ji4School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilong Jiang Province, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilong Jiang Province, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilong Jiang Province, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilong Jiang Province, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilong Jiang Province, ChinaThe application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.http://dx.doi.org/10.1155/2020/9150583 |
| spellingShingle | Longhai Yang Hong Xu Xiqiao Zhang Shuai Li Wenchao Ji Study on Fractal Multistep Forecast for the Prediction of Driving Behavior Journal of Advanced Transportation |
| title | Study on Fractal Multistep Forecast for the Prediction of Driving Behavior |
| title_full | Study on Fractal Multistep Forecast for the Prediction of Driving Behavior |
| title_fullStr | Study on Fractal Multistep Forecast for the Prediction of Driving Behavior |
| title_full_unstemmed | Study on Fractal Multistep Forecast for the Prediction of Driving Behavior |
| title_short | Study on Fractal Multistep Forecast for the Prediction of Driving Behavior |
| title_sort | study on fractal multistep forecast for the prediction of driving behavior |
| url | http://dx.doi.org/10.1155/2020/9150583 |
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