Open Accesstraffic flow prediction model based on deep belief network and genetic algorithm
Author(s) -
Zhang Yaying,
Huang Guan
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0199
Subject(s) - traffic flow (computer networking) , computer science , intelligent transportation system , genetic algorithm , algorithm , artificial intelligence , conjugate gradient method , flow network , flow (mathematics) , data mining , machine learning , engineering , mathematical optimization , transport engineering , mathematics , geometry , computer security
Traffic flow prediction plays an indispensable role in the intelligent transportation system. The effectiveness of traffic control and management relies heavily on the prediction accuracy. The authors propose a model based on deep belief networks (DBNs) to predict the traffic flow. Moreover, they use Fletcher–Reeves conjugate gradient algorithm to optimise the fine‐tuning of model's parameters. Since the traffic flow has various features at different times such as weekday, weekend, daytime and night‐time, the hyper‐parameters of the model should adapt to the time. Therefore, they employ the genetic algorithm to find the optimal hyper‐parameters of DBN models for different times. The dataset from Caltrans Performance Measurement System was used to evaluate the performance of their models. The experimental results demonstrate that the proposed model achieved better performance in different times.