Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting
Traffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traf...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/6933344 |
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| _version_ | 1832559172760109056 |
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| author | Ping Lou Zihao Wu Jiwei Hu Quan Liu Qin Wei |
| author_facet | Ping Lou Zihao Wu Jiwei Hu Quan Liu Qin Wei |
| author_sort | Ping Lou |
| collection | DOAJ |
| description | Traffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traffic flow has highly non-linear and complex patterns due to external factors such as time and space. Due to the high stochasticity and uncertainty of traffic flow, the difficulty of traffic flow prediction increases gradually with increasing time steps. The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, different methods are compared on the same time-granularity dataset, leaving the adaptability and robustness of these methods undervalidated. To address the above challenges, a new traffic forecasting method, named Attention-Based Gated Recurrent Graph Convolutional Network (AGRGCN) is presented for short-term traffic flow prediction. The method can extract spatialtemporal dependencies in traffic flow. In addition, an attention mechanism, which can adaptively capture traffic data relationships at different time steps, is introduced to alleviate the problem of faster deterioration of model prediction performance for longer time steps. Using a road network distance-based graph enables the method better to capture the topological information in traffic flow data. Experiments were conducted on two traffic datasets with different time granularity to predict traffic flow in highway and urban contexts. The experimental results show that our model has certain advantages. |
| format | Article |
| id | doaj-art-d48884b0b74b4d78811c55447b9db153 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-d48884b0b74b4d78811c55447b9db1532025-02-03T01:30:44ZengWileyJournal of Mathematics2314-47852023-01-01202310.1155/2023/6933344Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow ForecastingPing Lou0Zihao Wu1Jiwei Hu2Quan Liu3Qin Wei4School of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringTraffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traffic flow has highly non-linear and complex patterns due to external factors such as time and space. Due to the high stochasticity and uncertainty of traffic flow, the difficulty of traffic flow prediction increases gradually with increasing time steps. The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, different methods are compared on the same time-granularity dataset, leaving the adaptability and robustness of these methods undervalidated. To address the above challenges, a new traffic forecasting method, named Attention-Based Gated Recurrent Graph Convolutional Network (AGRGCN) is presented for short-term traffic flow prediction. The method can extract spatialtemporal dependencies in traffic flow. In addition, an attention mechanism, which can adaptively capture traffic data relationships at different time steps, is introduced to alleviate the problem of faster deterioration of model prediction performance for longer time steps. Using a road network distance-based graph enables the method better to capture the topological information in traffic flow data. Experiments were conducted on two traffic datasets with different time granularity to predict traffic flow in highway and urban contexts. The experimental results show that our model has certain advantages.http://dx.doi.org/10.1155/2023/6933344 |
| spellingShingle | Ping Lou Zihao Wu Jiwei Hu Quan Liu Qin Wei Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting Journal of Mathematics |
| title | Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting |
| title_full | Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting |
| title_fullStr | Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting |
| title_full_unstemmed | Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting |
| title_short | Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting |
| title_sort | attention based gated recurrent graph convolutional network for short term traffic flow forecasting |
| url | http://dx.doi.org/10.1155/2023/6933344 |
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