A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection

Thaika, Majeed; Tasneeyapant, Songwong; Cheamanunkul, Sunsern

doi:10.1109/jcsse.2018.8457338

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…e traffic flow data obtained in this paper mainly comes from the microwave detector data collected by Hangzhou urban expressway monitoring center in Hangzhou viaduct's section for 5 months (from June 11, 2015, to November 11, 2015). Among them, the sampling [12] interval of microwave traffic detection data is 1 min, and the collected data content is serial number, fixed detector number, date, time, flow, speed, and time occupancy. e traffic incident information source is the expressway incident information released by Hangzhou traffic information network.…”

Section: Data Description and Variable Selectionmentioning

confidence: 99%

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Fang

Yang

Zheng

et al. 2020

Mathematical Problems in Engineering

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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.

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Section: Data Description and Variable Selectionmentioning

confidence: 99%

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

Fang

Yang

Zheng

et al. 2020

Mathematical Problems in Engineering

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“…to extract features for furthering detect incidents [3], [4]. So far, unsupervised learning models have also been employed to develop some AID approaches.…”

Section: Automatic Incident Detectionmentioning

confidence: 99%

Machine learning based online traffic incident detection and management for urban networks

Yang¹

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He is not only my PhD supervisor, but also my life mentor.My sincere thanks to Dr. Wang Yu from I2R Astar, Singapore, for his kindly help and instruction in my research. I also want to thank my wonderful fellow lab mates, who have made my Ph.D journey more meaningful and enjoyable.Last but not least, I would like to express my sincere thanks to my family, my beloved wife Ms. Wang Zongfang, my father Mr. Yang Jianming, and my mother Ms. Bai Xuxia, for their constant love, encouragement and endless support. They give me the warmest harbourand. They are my solid backing, and my greatest motivation to achieve more in my whole life.

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