Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU
Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. Th...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8836595/ |
| _version_ | 1818935666756550656 |
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| author | Guowen Dai Changxi Ma Xuecai Xu |
| author_facet | Guowen Dai Changxi Ma Xuecai Xu |
| author_sort | Guowen Dai |
| collection | DOAJ |
| description | Accurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability. |
| first_indexed | 2024-12-20T05:23:48Z |
| format | Article |
| id | doaj.art-47484e36ae7349fdb37097cbb90bcddb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:23:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-47484e36ae7349fdb37097cbb90bcddb2022-12-21T19:51:56ZengIEEEIEEE Access2169-35362019-01-01714302514303510.1109/ACCESS.2019.29412808836595Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRUGuowen Dai0Changxi Ma1Xuecai Xu2https://orcid.org/0000-0001-5798-8441School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, ChinaAccurate short-term traffic forecasts help people choose transportation and travel time. Through the query data, many models for traffic flow prediction have neglected the temporal and spatial correlation of traffic flow, so that the prediction accuracy is limited by the accuracy of traffic data. This paper proposed a short-term traffic flow prediction model that combined the spatio-temporal analysis with a Gated Recurrent Unit (GRU). In the proposed prediction model, firstly, time correlation analysis and spatial correlation analysis were performed on the collected traffic flow data, and then the spatiotemporal feature selection algorithm was employed to define the optimal input time interval and spatial data volume. At the same time, the selected traffic flow data were extracted from the actual traffic flow data and converted into a two-dimensional matrix with spatio-temporal traffic flow information. The GRU was used to process the spatio-temporal feature information of the internal traffic flow of the matrix to achieve the purpose of prediction. Finally, the prediction results obtained by the proposed model were compared with the actual traffic flow data to verify the effectiveness of the model. The model proposed in this paper was compared with the convolutional neural network (CNN) model and the GRU model, and the results show that the proposed method outperforms both in accuracy and stability.https://ieeexplore.ieee.org/document/8836595/Gated recurrent unitspatio-temporal analysisshort-term traffic flow predictiontraffic engineeringurban road section |
| spellingShingle | Guowen Dai Changxi Ma Xuecai Xu Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU IEEE Access Gated recurrent unit spatio-temporal analysis short-term traffic flow prediction traffic engineering urban road section |
| title | Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU |
| title_full | Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU |
| title_fullStr | Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU |
| title_full_unstemmed | Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU |
| title_short | Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space–Time Analysis and GRU |
| title_sort | short term traffic flow prediction method for urban road sections based on space x2013 time analysis and gru |
| topic | Gated recurrent unit spatio-temporal analysis short-term traffic flow prediction traffic engineering urban road section |
| url | https://ieeexplore.ieee.org/document/8836595/ |
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