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

In: Operations Research Proceedings 2023

Author

Listed:
  • 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)

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.

Suggested Citation

  • Simon Rienks & Fiona Sauerbier & Knut Haase & Martin Spindler, 2025. "Crowd Management for the FIFA World Cup Qatar 2022 $$^\textrm{TM}$$ TM in Doha," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 485-491, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_62
    DOI: 10.1007/978-3-031-58405-3_62
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