With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.
An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support.
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