Mean-field type modeling of nonlocal crowd aversion in pedestrian crowd dynamics
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Abstract (arXiv)
We extend the class of pedestrian crowd models introduced by Lachapelle and Wolfram (2011) to allow for nonlocal crowd aversion and arbitrarily but finitely many interacting crowds. The new crowd aversion feature grants pedestrians a "personal space" where crowding is undesirable. We derive the model from a particle picture and treat it as a mean-field type game. Solutions to the mean-field type game are characterized via a Pontryagin-type Maximum Principle. The behavior of pedestrians acting under nonlocal crowd aversion is illustrated by a numerical simulation.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2017.
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Citing papers per year: 2021: 2; 2022: 2.
4 papers.
- 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approachescited by 30
- 2021Scaling up Mean Field Games with Online Mirror Descentcited by 29
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
- 2022Hypergraphon Mean Field Gamescited by 3
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning8 Sep 2022
- 2022Hypergraphon Mean Field Games30 Mar 2022
- 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches11 Nov 2021
- 2021Scaling up Mean Field Games with Online Mirror Descent28 Feb 2021