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Research on Time Performance Optimization of LSTM Model for Pedestrian Volume Prediction
10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 ; 2022-June:517-521, 2022.
Article in English | Scopus | ID: covidwho-2018926
ABSTRACT
The prediction of pedestrian volume in public area is of great significance to maintain personnel safety and improve the level of public management. Especially under the situation of COVID-19 prevention and control, the prediction of pedestrian volume within closed public spaces has a higher demand. While long short-term memory (LSTM), is often used to establish the prediction model of time series, for this purpose, taking the pedestrian flow prediction as the application background, the influence of the activation function on the time performance of LSTM model is studied, and an optimized scheme of the activation function, which can significantly improve the time performance while ensuring the prediction accuracy is proposed in this paper. The experimental results based on pedestrian flow prediction show that the time performance of the optimized LSTM model is improved by about 12.8% compared with the traditional model, and the prediction accuracy is even slightly increased. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 Year: 2022 Document Type: Article