Crowd Management for the FIFA World Cup Qatar 2022 $$^\textrm{TM}$$ TM in Doha

Simon Rienks (), Fiona Sauerbier (), Knut Haase () and Martin Spindler ()
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Simon Rienks: University of Hamburg, Institute of Logistics, Transport and Production
Fiona Sauerbier: University of Hamburg, Institute of Logistics, Transport and Production
Knut Haase: University of Hamburg, Institute of Logistics, Transport and Production
Martin Spindler: University of Hamburg, Institute of Statistics

Chapter Chapter 62 in Operations Research Proceedings 2023, 2025, pp 485-491 from Springer

Abstract: Abstract Prior to the FIFA World Cup Qatar 2022 $$^\textrm{TM}$$ TM , we conducted a survey regarding the use of different modes of transportation. This empirical research was performed to calibrate a passenger demand model to estimate the number of passengers at metro stations, considering a no-show rate and providing a basis for admission control. Decision theory and machine learning techniques were used to identify the key factors that influence transport choices. Interaction terms and binary variables from a decision tree created by the CART algorithm were included to capture non-linear effects. Maximum likelihood with a lasso penalty term was applied to estimate the high-dimensional utility function of the multinomial logit model, resulting in a sparse utility function. Our results illustrate that the use of a data-driven method can provide remarkably accurate predictions for transport mode choice analysis. This can be achieved without time-consuming and subjective preprocessing. Most importantly, no additional expert knowledge is required.

Keywords: Transport choice; Machine learning; Multinomial logit model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-58405-3_62

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