Traffic Accident Prediction Based on LSTM-GBRT Model
Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help ma...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/4206919 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546976724418560 |
|---|---|
| author | Zhihao Zhang Wenzhong Yang Silamu Wushour |
| author_facet | Zhihao Zhang Wenzhong Yang Silamu Wushour |
| author_sort | Zhihao Zhang |
| collection | DOAJ |
| description | Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels. |
| format | Article |
| id | doaj-art-da8a0c14724541a39546eb30ef9c2578 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-da8a0c14724541a39546eb30ef9c25782025-02-03T06:46:37ZengWileyJournal of Control Science and Engineering1687-52491687-52572020-01-01202010.1155/2020/42069194206919Traffic Accident Prediction Based on LSTM-GBRT ModelZhihao Zhang0Wenzhong Yang1Silamu Wushour2College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaRoad traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.http://dx.doi.org/10.1155/2020/4206919 |
| spellingShingle | Zhihao Zhang Wenzhong Yang Silamu Wushour Traffic Accident Prediction Based on LSTM-GBRT Model Journal of Control Science and Engineering |
| title | Traffic Accident Prediction Based on LSTM-GBRT Model |
| title_full | Traffic Accident Prediction Based on LSTM-GBRT Model |
| title_fullStr | Traffic Accident Prediction Based on LSTM-GBRT Model |
| title_full_unstemmed | Traffic Accident Prediction Based on LSTM-GBRT Model |
| title_short | Traffic Accident Prediction Based on LSTM-GBRT Model |
| title_sort | traffic accident prediction based on lstm gbrt model |
| url | http://dx.doi.org/10.1155/2020/4206919 |
| work_keys_str_mv | AT zhihaozhang trafficaccidentpredictionbasedonlstmgbrtmodel AT wenzhongyang trafficaccidentpredictionbasedonlstmgbrtmodel AT silamuwushour trafficaccidentpredictionbasedonlstmgbrtmodel |