Search

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

The translation of pragmatic meaning requires the processing of pragmatic implicit meaning, the translator's responsibility for the readers of the target language to understand the pragmatic meaning, and the domestication and foreignization of cultural images. The way of pragmatic language equivalence is domestication translation to achieve the fluency of language expression. The creation of foreignized translation in order to foster the equitable interchange and dissemination of culture, which is precisely the aim of cultural translation, is the equivalence of the pragmatic meaning of cultural imagery. Neural network (NN) machine translation technology (MTT) simulates the human brain nervous system. Based on deep learning technology, the whole sentence is used as the basic unit of translation, which greatly improves the accuracy of machine translation. This paper selects the relevant texts of Shaanxi's red tourism culture and uses the NN translation system to translate. It helps to improve the accuracy of the text translation of Shaanxi red tourism culture by analyzing the translation and providing a reference direction for further revision by neural translation users and promote the overseas dissemination of red culture. The part of the source language side that generates a vector representation of a dense and continuous sentence from the input corresponds to the encoder. [ABSTRACT FROM AUTHOR]