Enhancing Urban Crowd Monitoring through Predictive Modelling System with Diverse Geospatial Datasets

The escalating urban population had resulted in social and safety challenges. Therefore, effectively managing crowd congestion in densely populated cities became of utmost importance in urban governance. In order to address urban challenges, we proposed a monitor model based on the GRU time-series model, integrating data from telecommunications, ticket sales, events, weather observations, and parking availability to predict and control urban crowds. In this study, the GRU model outperformed LSTM and GRU-Attention models due to its efficiency. Taking six types of hourly data from the past 48 hours as input, it forecasted tourist flow at attractions five hours ahead. Additionally, a visualization system was developed to allow users to analyze historical data, specific attractions, and prediction times. The proposed system offered valuable tools for urban crowd monitoring, facilitating informed decision-making, resource allocation, and efficient governance and tourism activities.