Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events
Panchamy Krishnakumari, Sascha Hoogendoorn-Lanser, Jeroen, Steenbakkers, Serge Hoogendoorn

TL;DR
This paper introduces a data-driven framework using AI, digital twins, and real-time data to improve crowd management and risk prediction at large events, demonstrated through a case study in Scheveningen.
Contribution
It presents a novel integrated approach combining data collection, visualization, and AI-based risk assessment with multi-day forecasting for crowd management.
Findings
XGBoost outperforms other models in forecasting accuracy
Predictions are sufficiently accurate for practical use
Additional data could improve location-specific forecasts
Abstract
This paper presents novel technology and methodology aimed at enhancing crowd management in both the planning and operational phases. The approach encompasses innovative data collection techniques, data integration, and visualization using a 3D Digital Twin, along with the incorporation of artificial intelligence (AI) tools for risk identification. The paper introduces the Bowtie model, a comprehensive framework designed to assess and predict risk levels. The model combines objective estimations and predictions, such as traffic flow operations and crowdedness levels, with various aggravating factors like weather conditions, sentiments, and the purpose of visitors, to evaluate the expected risk of incidents. The proposed framework is applied to the Crowd Safety Manager project in Scheveningen, where the DigiTwin is developed based on a wealth of real-time data sources. One noteworthy…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Evacuation and Crowd Dynamics · Traffic and Road Safety
MethodsFocus
