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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. [ABSTRACT FROM AUTHOR]

In recent years, China has been undergoing a metro railway construction boom in order to alleviate the urban traffic congestion problem resulting from the rapid urbanization and population growth in many metropolises. In the construction of metro systems, deep excavations and continuous dewatering for construction of the metro tunnels and stations remain a challenging and high risk task in densely populated urban areas. Intelligent computational methods and techniques have exhibited the exceptional talent in dealing with the complicated problems inherent in the deep excavation and dewatering practice. In this paper, an intelligent risk assessment system for deep excavation dewatering is developed and has been applied in the project of HangzhouMetro Line 1 which is the first metro line of the urban rapid rail transit system in Hangzhou, China. The specific characteristics and great challenges in deep excavation dewatering of the metro-tunnel airshaft of Hangzhou Metro Line 1 are addressed. A novel design method based on the coupled three-dimensional flow theory for dewatering of the deep excavation is introduced. The modularly designed system for excavation dewatering risk assessment is described, and the field observations in dewatering risk assessment of the airshaft excavation of Hangzhou Metro Line 1 are also presented. [ABSTRACT FROM AUTHOR]