Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction
With the rapid development of transport technology and the increasing complexity of traffic patterns, integrating multiple data sources for traffic flow prediction has become crucial to overcome the defects of a single data source. This paper introduces a multisource data fusion approach with graph...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/7109780 |
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| _version_ | 1846122276258316288 |
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| author | Lei Huang Jianxin Qin Tao Wu |
| author_facet | Lei Huang Jianxin Qin Tao Wu |
| author_sort | Lei Huang |
| collection | DOAJ |
| description | With the rapid development of transport technology and the increasing complexity of traffic patterns, integrating multiple data sources for traffic flow prediction has become crucial to overcome the defects of a single data source. This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. In addition, a GCN combined with gated recurrent units (GRUs) is proposed for spatiotemporal modeling of data fusion and traffic flow prediction. The main contributions are: (1) The approach significantly improved prediction accuracy by leveraging multiple data sources compared to a single source. (2) A unified graph structure was created via global traffic nodes to interpolate traffic flow and address data sparsity. (3) The proposed model demonstrates an over 11% improvement in accuracy compared to other baseline models, as measured by the weighted mean absolute percentage error (WMAPE). It also exhibits stability in multitime scale predictions, highlighting the effectiveness of multisource data fusion, data imputation, and node-level prediction capabilities. The approach provides valuable insights for managing urban traffic data from multiple sources and predicting traffic flow, and it shows stability in multitime scale predictions. |
| format | Article |
| id | doaj-art-c5c64df9c9dd46af9fa10bc3b3dc5d8b |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c5c64df9c9dd46af9fa10bc3b3dc5d8b2024-12-15T00:00:01ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/atr/7109780Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow PredictionLei Huang0Jianxin Qin1Tao Wu2Hunan Key Laboratory of Geospatial Big Data Mining and ApplicationHunan Key Laboratory of Geospatial Big Data Mining and ApplicationHunan Key Laboratory of Geospatial Big Data Mining and ApplicationWith the rapid development of transport technology and the increasing complexity of traffic patterns, integrating multiple data sources for traffic flow prediction has become crucial to overcome the defects of a single data source. This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. In addition, a GCN combined with gated recurrent units (GRUs) is proposed for spatiotemporal modeling of data fusion and traffic flow prediction. The main contributions are: (1) The approach significantly improved prediction accuracy by leveraging multiple data sources compared to a single source. (2) A unified graph structure was created via global traffic nodes to interpolate traffic flow and address data sparsity. (3) The proposed model demonstrates an over 11% improvement in accuracy compared to other baseline models, as measured by the weighted mean absolute percentage error (WMAPE). It also exhibits stability in multitime scale predictions, highlighting the effectiveness of multisource data fusion, data imputation, and node-level prediction capabilities. The approach provides valuable insights for managing urban traffic data from multiple sources and predicting traffic flow, and it shows stability in multitime scale predictions.http://dx.doi.org/10.1155/atr/7109780 |
| spellingShingle | Lei Huang Jianxin Qin Tao Wu Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction Journal of Advanced Transportation |
| title | Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction |
| title_full | Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction |
| title_fullStr | Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction |
| title_full_unstemmed | Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction |
| title_short | Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction |
| title_sort | multisource data fusion with graph convolutional neural networks for node level traffic flow prediction |
| url | http://dx.doi.org/10.1155/atr/7109780 |
| work_keys_str_mv | AT leihuang multisourcedatafusionwithgraphconvolutionalneuralnetworksfornodeleveltrafficflowprediction AT jianxinqin multisourcedatafusionwithgraphconvolutionalneuralnetworksfornodeleveltrafficflowprediction AT taowu multisourcedatafusionwithgraphconvolutionalneuralnetworksfornodeleveltrafficflowprediction |