Discrete Choice Models for Pedestrian Walking Behavior

By G. Antonini, Michel Bierlaire. 2006

565 citations recorded by the source corpus.

Subfields: Nature and Landscape Conservation, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b7899a01cd9b6c7c96d0bbfc4e657fe9907f0ccd

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In Nature and Landscape Conservation

In Ocean Engineering

Citations in the corpus

Large language models empowered agent-based modeling and simulation: a survey and perspectives (2023, unknown): “For example, to account for the impact of randomness originating from human decision-making, we can leverage discrete choice models for simulating pedestrian walking behaviors (Antonini et al., 2006).”
The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics (2010, background): “argely neglected. Indeed, the great majority of existing studies investigated a crowd as a collection of isolated individuals, each having an own desired speed and direction of motion, see e.g. Refs. [9,10,13,14]. In practice, however, it turns out that the majority of pedestrians actually do not wa”
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction (2019, methodology): “There are also other hand-crafted features based models, where Antonini et al . used a Discrete Choice framework [3], Treuille et al . proposed continuum dynamics [31], and further, there are a series of topics models [35, 15, 9].”
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks (2018, methodology): “The interpersonal aspect has been exhaustively addressed by traditional methods based on hand-crafted features [2, 17, 41, 46].”
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning (2018, methodology): “, based on Discrete Choice models [38]) have a nice interpretable property but have limited prediction power as recently shown in [39].”
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes (2016, methodology): “use the Discrete Choice Model to synthesize human trajectories in crowded scenes [20,21].”
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting (2022, unknown): “Pi-oneering works take advantage of hand-crafted relations, energy-based features or rule-based models [3, 16, 26, 40, 44], which fail to adapt to scene changes and model complex crowd dynamics.”
Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016, methodology): “[2] propose a Discrete Choice framework and Wang et.”
Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions (2011, unknown): “The bulk of the literature is restricted to the study of normal (non-panic) pedestrian dynamics or normal evacuation processes (Still 2000, Hoogendoorn & Bovy 2002, Hoogendoorn 2004, Daamen 2004, Antonini et al. 2006, Kretz 2007, Asano 2009).”