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Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
- Source :
- IEEE Access, Vol 7, Pp 123981-123991 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.
- Subjects :
- 0209 industrial biotechnology
deep belief network
General Computer Science
Computer science
Real-time computing
General Engineering
02 engineering and technology
Short-term traffic prediction
Term (time)
Deep belief network
020901 industrial engineering & automation
edge computing
ComputerSystemsOrganization_MISCELLANEOUS
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
Hidden Markov model
hidden Markov model
lcsh:TK1-9971
Edge computing
5G
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....7fc41ce30bc1616c4a10e03d961868e7