期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 14, 期 8, 页码 936-946出版社
WILEY
DOI: 10.1049/iet-its.2019.0778
关键词
telecommunication traffic; learning (artificial intelligence); traffic engineering computing; road traffic; recurrent neural nets; neural nets; model learning; training data; traffic incidents; traffic data; traffic simulations; deep learning model; graph convolution; traffic incident information features; prediction accuracy; short-term memory; traffic flow; term prediction; incident conditions; graph convolutional recurrent neural network; traffic simulation; unusual conditions; machine-learning-based traffic prediction
资金
- JSPS KAKENHI [15H01785]
- Grants-in-Aid for Scientific Research [15H01785] Funding Source: KAKEN
The objective of the study is to predict traffic flow under unusual conditions by using a deep learning model. Conventionally, machine-learning-based traffic prediction is frequently carried out. Model learning requires large amounts of training data; however, collecting sufficient samples is a challenge in the event of traffic incidents. To address this challenge, large amounts of traffic data were generated by performing traffic simulations under various traffic incidents. These data were used as training data, and a deep learning model with graph convolution and input of traffic incident information features was proposed. Subsequently, the prediction accuracy was compared with other models such as long short-term memory, which is typically used in traffic prediction. The results demonstrated the superiority of the proposed model in representing phenomena with strong spatio-temporal dependencies, such as traffic flow, and its effectiveness in traffic prediction.
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