Deep learning-based crowd recognition for tourist attractions in different periods

Abstract

The accuracy of tourists traffic prediction plays a critical role in scenic area management. Traditional methods of forecasting tourist attraction traffic rely heavily on static historical data, often ignoring important factors that affect the flow of tourists. This process is usually time-consuming. However, with the emergence of deep technology, it is now possible to use real-time data collection and analysis to design a temporal and spatial representation of data sources. And a deep learning-based tourist flow recognition model combined with dynamic time-bending distance indicators and a temporal feature recognition method with temporal data clustering analysis is designed. The method can use location big data to analyze traffic temporal types and identify traffic spatial distribution features, and the analysis results can help traffic and facility management in scenic areas.