Urban Traffic Flow Forecast Based on FastGCRNN
Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. Howe...
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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/10.1155/2020/8859538 |
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| author | Ya Zhang Mingming Lu Haifeng Li |
| author_facet | Ya Zhang Mingming Lu Haifeng Li |
| author_sort | Ya Zhang |
| collection | DOAJ |
| description | Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy. |
| format | Article |
| id | doaj-art-d0909c4a12ec47ae82a6f8b4a899c94a |
| 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-d0909c4a12ec47ae82a6f8b4a899c94a2025-02-03T06:46:08ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88595388859538Urban Traffic Flow Forecast Based on FastGCRNNYa Zhang0Mingming Lu1Haifeng Li2School of Computer Science and Engineering, Central South University, Changsha 410083, Hunan, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, Hunan, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, ChinaTraffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.http://dx.doi.org/10.1155/2020/8859538 |
| spellingShingle | Ya Zhang Mingming Lu Haifeng Li Urban Traffic Flow Forecast Based on FastGCRNN Journal of Advanced Transportation |
| title | Urban Traffic Flow Forecast Based on FastGCRNN |
| title_full | Urban Traffic Flow Forecast Based on FastGCRNN |
| title_fullStr | Urban Traffic Flow Forecast Based on FastGCRNN |
| title_full_unstemmed | Urban Traffic Flow Forecast Based on FastGCRNN |
| title_short | Urban Traffic Flow Forecast Based on FastGCRNN |
| title_sort | urban traffic flow forecast based on fastgcrnn |
| url | http://dx.doi.org/10.1155/2020/8859538 |
| work_keys_str_mv | AT yazhang urbantrafficflowforecastbasedonfastgcrnn AT mingminglu urbantrafficflowforecastbasedonfastgcrnn AT haifengli urbantrafficflowforecastbasedonfastgcrnn |