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The current development plans for logistics of city clusters often pursue investment speed, while overlooking the economic benefits of logistics within city clusters. To prevent the waste of logistics resources, it is meaningful to study the meaning of the logistics radiation ability of city clusters and determine their logistics radiation range. Taking Ningbo Metropolitan Area as an example, this paper tries to measure logistics radiation range and improve logistics radiation ability of city clusters. The analytic hierarchy process (AHP) was adopted to quantify the logistics influencing factors. Besides, an improved breakpoint model was established based on the breakpoint theory, the logistics radiation range model, and the integrated logistics field theory. Drawing on the statistics in 2017, the improved model was applied to empirically measure the variation of integrated logistics quality of eight cities and determine the logistics radiation ranges of Ningbo Metropolitan Area relative to Shanghai Metropolitan Area and Hangzhou Metropolitan Area. Finally, several countermeasures were developed to improve the logistics radiation ability of Ningbo Metropolitan Area: the positioning of metropolitan areas, the model of logistics development, the development of industries closely associated with logistics, and the cultivation of strategic locational advantages. The research provides a good reference for the government of Ningbo to develop advantageous logistics fields around advantages and turn the city into an economic center. [ABSTRACT FROM AUTHOR]

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]