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A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection
Joint Authors
Yang, Qing-fang
Zheng, Lili
Fang, YunFeng
Zhou, Xiangyu
Peng, Bo
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways.
Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety.
At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables.
This paper presents an automatic event detection algorithm based on deep cycle limit learning machine.
The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network.
The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search.
The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved.
In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer.
Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods.
The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information.
It can save expensive computing resources and reduce the complexity of the model.
Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%.
It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms.
It is suitable for urban expressway traffic incident detection.
American Psychological Association (APA)
Fang, YunFeng& Yang, Qing-fang& Zheng, Lili& Zhou, Xiangyu& Peng, Bo. 2020. A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1196426
Modern Language Association (MLA)
Fang, YunFeng…[et al.]. A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1196426
American Medical Association (AMA)
Fang, YunFeng& Yang, Qing-fang& Zheng, Lili& Zhou, Xiangyu& Peng, Bo. A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1196426
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196426