# Short‐term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/short-term-prediction-of-traffic-flow-under-incident-conditions-using-graph/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shota Fukuda,Hideaki Uchida,Hideki Fujii,Tomonori Yamada |
| Citations | 58 |
| DOI | 10.1049/iet-its.2019.0778 |
| Fields | Engineering,Social Sciences |
| Open Access | true |
| OA Status | bronze |
| OA URL | https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-its.2019.0778 |
| OpenAlex ID | https://openalex.org/W3021626758 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Hideaki Uchida](https://scholariq.org/researchers/hideaki-uchida/)

## Paper primary topic

- [Traffic Prediction and Management Techniques](https://scholariq.org/topics/traffic-prediction-and-management-techniques/)

## Paper topics

- [Traffic Prediction and Management Techniques](https://scholariq.org/topics/traffic-prediction-and-management-techniques/)
- [Traffic control and management](https://scholariq.org/topics/traffic-control-and-management/)
- [Transportation Planning and Optimization](https://scholariq.org/topics/transportation-planning-and-optimization/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.