Hybrid radial basis function neural networks for urban traffic signal control
Author
Source
Journal of Engineering Research
Issue
Vol. 8, Issue 4 (31 Dec. 2020), pp.153-168, 16 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2020-12-31
Country of Publication
Kuwait
No. of Pages
16
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
In this study, a real-world isolated signalized intersection with a fixed-time signal control system is considered.
The signal timing plans are arranged regardless of the traffic density, and these plans cause delays in vehicle queues.
To increase the efficiency of the intersection, an adaptive traffic signal control system is proposed to manage the intersection.
To find the appropriate adaptive green times for each lane, simulations are performed by traffic simulation software using vehicle arrivals and other information about vehicle movements gathered from the real-world intersection.
Then, a hybrid radial basis function neural network is developed to forecast the adaptive green times, which is trained and tested with historical arrivals and simulation results.
The performance of the proposed network is compared with well-known data mining classification methods, such as support vector regression, k-nearest neighbors, decision tree, random forest, and multilayer perceptron methods, by different evaluation parameters.
The comparison results provide that the developed radial basis function neural network outperforms other classification methods and can be successfully used for forecasting adaptive green times as an alternative to complex unsupervised classification methods.
American Psychological Association (APA)
Gencosman, Burcu Caglar. 2020. Hybrid radial basis function neural networks for urban traffic signal control. Journal of Engineering Research،Vol. 8, no. 4, pp.153-168.
https://search.emarefa.net/detail/BIM-1494678
Modern Language Association (MLA)
Gencosman, Burcu Caglar. Hybrid radial basis function neural networks for urban traffic signal control. Journal of Engineering Research Vol. 8, no. 4 (Dec. 2020), pp.153-168.
https://search.emarefa.net/detail/BIM-1494678
American Medical Association (AMA)
Gencosman, Burcu Caglar. Hybrid radial basis function neural networks for urban traffic signal control. Journal of Engineering Research. 2020. Vol. 8, no. 4, pp.153-168.
https://search.emarefa.net/detail/BIM-1494678
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 166-168
Record ID
BIM-1494678