Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN
As an essential parameter to represent vehicle following characteristics, distance headway (DHW) plays an essential role in microtraffic flow simulation, traffic control, and traffic safety alarm. However, due to the randomness, nonlinearity, and correlation of DHW data, constructing DHW prediction...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6456186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849703561955377152 |
|---|---|
| author | Chun Wang Weihua Zhang Cong Wu Heng Hu Wenjia Zhu |
| author_facet | Chun Wang Weihua Zhang Cong Wu Heng Hu Wenjia Zhu |
| author_sort | Chun Wang |
| collection | DOAJ |
| description | As an essential parameter to represent vehicle following characteristics, distance headway (DHW) plays an essential role in microtraffic flow simulation, traffic control, and traffic safety alarm. However, due to the randomness, nonlinearity, and correlation of DHW data, constructing DHW prediction models is difficult. Moreover, few studies have considered the time correlation between the historical DHW and the target DHW. To solve the above problems, a DHW prediction model is proposed in this paper by integrating entropy-based grey relation analysis (EB-GRA) and temporal convolutional network (TCN), named as EB-GRA-TCN model. In the model, the EB-GRA is adopted to calculate the correlation between the target DHW and historical DHW sequences, and the DHW data with high correlation are dynamically selected as the optimal input of the DHW prediction model. Then, the TCN algorithm is used to train the DHW prediction model. The TCN architecture integrates the advantages of recurrent neural network (RNN) and convolutional neural network (CNN), which could fully use the previous DHW information. In the experiment, the DHW data from Hefei Expressway are utilized for training the EB-GRA-TCN model. The prediction results showed that the average root mean square error (RMSE) and mean absolute error (MAE) of the proposed model were 0.115 and 0.090, respectively, in the 5, 10, and 15 predicted steps. Compared with the autoregressive integrated moving average (ARIMA), TCN, RNN, and long short-term memory (LSTM) models, the EB-GRA-TCN model achieved the best prediction accuracy. The results indicated that the EB-GRA-TCN model obtained good predictive performance and could provide support for road traffic control and traffic safety warming. |
| format | Article |
| id | doaj-art-0c2b6fa8f56e47beb4defd3c7f316d39 |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0c2b6fa8f56e47beb4defd3c7f316d392025-08-20T03:17:13ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6456186Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCNChun Wang0Weihua Zhang1Cong Wu2Heng Hu3Wenjia Zhu4Hefei University of TechnologyHefei University of TechnologyHefei University of Technology Design Institute (Group) Co. LtdAnhui Baicheng Huitong Technology Co. LtdAnhui Baicheng Huitong Technology Co. LtdAs an essential parameter to represent vehicle following characteristics, distance headway (DHW) plays an essential role in microtraffic flow simulation, traffic control, and traffic safety alarm. However, due to the randomness, nonlinearity, and correlation of DHW data, constructing DHW prediction models is difficult. Moreover, few studies have considered the time correlation between the historical DHW and the target DHW. To solve the above problems, a DHW prediction model is proposed in this paper by integrating entropy-based grey relation analysis (EB-GRA) and temporal convolutional network (TCN), named as EB-GRA-TCN model. In the model, the EB-GRA is adopted to calculate the correlation between the target DHW and historical DHW sequences, and the DHW data with high correlation are dynamically selected as the optimal input of the DHW prediction model. Then, the TCN algorithm is used to train the DHW prediction model. The TCN architecture integrates the advantages of recurrent neural network (RNN) and convolutional neural network (CNN), which could fully use the previous DHW information. In the experiment, the DHW data from Hefei Expressway are utilized for training the EB-GRA-TCN model. The prediction results showed that the average root mean square error (RMSE) and mean absolute error (MAE) of the proposed model were 0.115 and 0.090, respectively, in the 5, 10, and 15 predicted steps. Compared with the autoregressive integrated moving average (ARIMA), TCN, RNN, and long short-term memory (LSTM) models, the EB-GRA-TCN model achieved the best prediction accuracy. The results indicated that the EB-GRA-TCN model obtained good predictive performance and could provide support for road traffic control and traffic safety warming.http://dx.doi.org/10.1155/2022/6456186 |
| spellingShingle | Chun Wang Weihua Zhang Cong Wu Heng Hu Wenjia Zhu Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN Journal of Advanced Transportation |
| title | Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN |
| title_full | Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN |
| title_fullStr | Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN |
| title_full_unstemmed | Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN |
| title_short | Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN |
| title_sort | combined prediction method of short term distance headway based on eb gra tcn |
| url | http://dx.doi.org/10.1155/2022/6456186 |
| work_keys_str_mv | AT chunwang combinedpredictionmethodofshorttermdistanceheadwaybasedonebgratcn AT weihuazhang combinedpredictionmethodofshorttermdistanceheadwaybasedonebgratcn AT congwu combinedpredictionmethodofshorttermdistanceheadwaybasedonebgratcn AT henghu combinedpredictionmethodofshorttermdistanceheadwaybasedonebgratcn AT wenjiazhu combinedpredictionmethodofshorttermdistanceheadwaybasedonebgratcn |