TY - GEN
T1 - Enhancing Urban Crowd Monitoring through Predictive Modelling System with Diverse Geospatial Datasets
AU - Hsieh, Hsun Ping
AU - Lee, Tzu Chang
AU - Lai, Shih Yu
AU - Tsai, Pei Chi
AU - Hsieh, Tzu Hsin
N1 - Publisher Copyright:
Copyright © 2023 Hsun-Ping Hsieh, Tzu-Chang Lee, Shih-Yu Lai, Pei-Chi Tsai, Tzu-Hsin Hsieh.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85192553789&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85192553789
T3 - International Conference on Information Systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies"
BT - International Conference on Information Systems, ICIS 2023
PB - Association for Information Systems
T2 - 44th International Conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023
Y2 - 10 December 2023 through 13 December 2023
ER -
