Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction
As a typical time series, the length of the data sequence is critical to the accuracy of traffic state prediction. In order to fully explore the causality between traffic data, this study established a temporal backtracking and multistep delay model based on recurrent neural networks (RNNs) to learn...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8899478 |
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| _version_ | 1849306806089678848 |
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| author | Licheng Qu Minghao Zhang Zhaolu Li Wei Li |
| author_facet | Licheng Qu Minghao Zhang Zhaolu Li Wei Li |
| author_sort | Licheng Qu |
| collection | DOAJ |
| description | As a typical time series, the length of the data sequence is critical to the accuracy of traffic state prediction. In order to fully explore the causality between traffic data, this study established a temporal backtracking and multistep delay model based on recurrent neural networks (RNNs) to learn and extract the long- and short-term dependencies of the traffic state data. With a real traffic data set, the coordinate descent algorithm was employed to search and determine the optimal backtracking length of traffic sequence, and multistep delay predictions were performed to demonstrate the relationship between delay steps and prediction accuracies. Besides, the performances were compared between three variants of RNNs (LSTM, GRU, and BiLSTM) and 6 frequently used models, which are decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), random forest (RF), gradient boosting decision tree (GBDT), and stacked autoencoder (SAE). The prediction results of 10 consecutive delay steps suggest that the accuracies of RNNs are far superior to those of other models because of the more powerful and accurate pattern representing ability in time series. It is also proved that RNNs can learn and mine longer time dependencies. |
| format | Article |
| id | doaj-art-278c07a3b9d4487cb35fc2ccee04b3f0 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-278c07a3b9d4487cb35fc2ccee04b3f02025-08-20T03:54:57ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88994788899478Temporal Backtracking and Multistep Delay of Traffic Speed Series PredictionLicheng Qu0Minghao Zhang1Zhaolu Li2Wei Li3School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, ChinaAs a typical time series, the length of the data sequence is critical to the accuracy of traffic state prediction. In order to fully explore the causality between traffic data, this study established a temporal backtracking and multistep delay model based on recurrent neural networks (RNNs) to learn and extract the long- and short-term dependencies of the traffic state data. With a real traffic data set, the coordinate descent algorithm was employed to search and determine the optimal backtracking length of traffic sequence, and multistep delay predictions were performed to demonstrate the relationship between delay steps and prediction accuracies. Besides, the performances were compared between three variants of RNNs (LSTM, GRU, and BiLSTM) and 6 frequently used models, which are decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), random forest (RF), gradient boosting decision tree (GBDT), and stacked autoencoder (SAE). The prediction results of 10 consecutive delay steps suggest that the accuracies of RNNs are far superior to those of other models because of the more powerful and accurate pattern representing ability in time series. It is also proved that RNNs can learn and mine longer time dependencies.http://dx.doi.org/10.1155/2020/8899478 |
| spellingShingle | Licheng Qu Minghao Zhang Zhaolu Li Wei Li Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction Journal of Advanced Transportation |
| title | Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction |
| title_full | Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction |
| title_fullStr | Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction |
| title_full_unstemmed | Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction |
| title_short | Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction |
| title_sort | temporal backtracking and multistep delay of traffic speed series prediction |
| url | http://dx.doi.org/10.1155/2020/8899478 |
| work_keys_str_mv | AT lichengqu temporalbacktrackingandmultistepdelayoftrafficspeedseriesprediction AT minghaozhang temporalbacktrackingandmultistepdelayoftrafficspeedseriesprediction AT zhaoluli temporalbacktrackingandmultistepdelayoftrafficspeedseriesprediction AT weili temporalbacktrackingandmultistepdelayoftrafficspeedseriesprediction |