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  "access_rights": "",
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  "author": "Zhengdao Li",
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  "defined_in": "https://doi.org/10.48550/arXiv.2307.05517",
            
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  "doi": "10.57702/3ncedg73",
            
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  "doi_date_published": "2024-12-03",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Wei Li",
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      "extra_author": "Wei Li",
            
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Kai Hwang",
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      "extra_author": "Kai Hwang",
            
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      "orcid": ""
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      "orcid": ""
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Traffic Flow Forecasting",
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      "display_name": "Traffic Flow Forecasting",
            
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      "name": "traffic-flow-forecasting",
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      "name": "traffic-flow-forecasting",
            
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      "title": "Traffic Flow Forecasting"
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      "title": "Traffic Flow Forecasting"
            
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    }
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    }
            
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  "name": 
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"adaptive-graph-convolution-networks-for-traffic-flow-forecasting",
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"adaptive-graph-convolution-networks-for-traffic-flow-forecasting",
            
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  "notes": "Traffic flow forecasting is a highly challenging task due 
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  "notes": "Traffic flow forecasting is a highly challenging task due 
            
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to the dynamic spatial-temporal road conditions. Graph neural networks 
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to the dynamic spatial-temporal road conditions. Graph neural networks 
            
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(GNN) has been widely applied in this task. However, most of these 
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(GNN) has been widely applied in this task. However, most of these 
            
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GNNs ignore the effects of time-varying road conditions due to the 
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GNNs ignore the effects of time-varying road conditions due to the 
            
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fixed range of the convolution receptive field.",
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fixed range of the convolution receptive field.",
            
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      "display_name": "graph neural networks",
            
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      "name": "time-varying road conditions",
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      "name": "time-varying road conditions",
            
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    {
            
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      "display_name": "traffic flow forecasting",
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      "display_name": "traffic flow forecasting",
            
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      "id": "82445629-4701-4c32-88d1-a3fa79eee946",
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      "id": "82445629-4701-4c32-88d1-a3fa79eee946",
            
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      "name": "traffic flow forecasting",
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      "name": "traffic flow forecasting",
            
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      "state": "active",
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    }
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  ],
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  ],
            
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  "title": "Adaptive Graph Convolution Networks for Traffic Flow 
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  "title": "Adaptive Graph Convolution Networks for Traffic Flow 
            
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Forecasting",
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Forecasting",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}