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
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 data
source is Resono, offering insights into the number of visitors and their
movements, leveraging a mobile phone panel of over 2 million users in the
Netherlands. Particular attention is given to the left-hand side of the Bowtie,
which includes state estimation, prediction, and forecasting. Notably, the
focus is on generating multi-day ahead forecasts for event-planning purposes
using Resono data. Advanced machine learning techniques, including the XGBoost
framework, are compared, with XGBoost demonstrating the most accurate
forecasts. The results indicate that the predictions are adequately accurate.
However, certain locations may benefit from additional input data to further
enhance prediction quality. Despite these limitations, this work contributes to
a more effective crowd management system and opens avenues for further
advancements in this critical field.