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.
Short‐term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation
IET Intelligent Transport Systems ; 14 , 8 ; 936-946
2020-08-01
11 pages
Article (Journal)
Electronic Resource
English
traffic incidents , incident conditions , recurrent neural nets , traffic simulations , traffic incident information features , neural nets , machine‐learning‐based traffic prediction , traffic flow , road traffic , model learning , short‐term memory , traffic data , traffic engineering computing , unusual conditions , training data , learning (artificial intelligence) , graph convolution , traffic simulation , term prediction , graph convolutional recurrent neural network , prediction accuracy , deep learning model , telecommunication traffic
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