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Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning
- Source :
- IEEE Access, Vol 7, Pp 142272-142279 (2019)
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Passenger flow prediction is the key to operation efficiency and safety of urban rail transit (URT). This paper combines the deep learning (DL) theory and support vector machine (SVM) into the DL-SVM model for URT passenger flow prediction. Firstly, the deep belief network (DBN) was adopted to extract the features and inherent variation of passenger flow data. On this basis, an SVM regression model was constructed to predict passenger flow. Then, the proposed model was compared with three shallow prediction models through experiments on Qingdao Metro. The results show that the DL-SVM outperformed the other models in accuracy and stability. The research findings shed important new light on the passenger flow prediction in the URT system.
- Subjects :
- Urban rail transit
General Computer Science
Computer science
urban rail transit (URT)
Big data
Stability (learning theory)
ComputerApplications_COMPUTERSINOTHERSYSTEMS
computer.software_genre
Deep belief network
deep belief network (DBN)
General Materials Science
business.industry
Deep learning
General Engineering
Big data analysis
Support vector machine
passenger flow prediction
Flow (mathematics)
support vector machine (SVM)
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
Data mining
business
lcsh:TK1-9971
computer
Predictive modelling
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....b545fe3db3279a5b5bfee5cc26a80343