Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method

Corbetta, Alessandro; Muntean, Adrian; Vafayi, Kiamars

doi:10.3934/mbe.2015.12.337

Cited by 52 publications

(49 citation statements)

References 35 publications

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“…Merged depth images were processed to detect pedestrian positions via the stochastic clustering algorithm proposed independently in [25] and [26]. We employed the same implementation of our previous works [12,23,33], to which we refer for further details.…”

Section: Appendix A: Pedestrian Tracking At Eindhoven Train Station: mentioning

confidence: 99%

Physics-based modeling and data representation of pairwise interactions among pedestrians

Corbetta¹,

Meeusen²,

Lee³

et al. 2018

Phys. Rev. E

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The possibility to understand and to quantitatively model the physics of the interactions between pedestrians walking in crowds has compelling relevant applications, e.g. related to the efficient design and the safety of civil infrastructures. In this work we study pedestrian-pedestrian interactions from observational experimental data in diluted pedestrian crowds. While in motion, pedestrians continuously adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. In mathematical models this behavior is typically modeled via "social" interaction forces.Leveraging on a high-quality, high-statistics dataset -composed of few millions of real-life trajectories acquired from state of the art observational experiments (about 6 months of high-resolution pedestrian tracks acquired in a train station) -we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long-range (sight based) and short-range (hard-contact avoidance) forces, which we superimpose to our Langevin model for non-interacting pedestrian motion [Corbetta et al. Phys. Rev. E 95, 032316, 2017] (here further tested and extended). The new model that we propose here features a Langevin dynamics with "fast" random velocity fluctuations that are superimposed to the "slow" dynamics of a hidden model variable: the "intended" walking path. In case of interactions, social forces may act both on the intended path and on the actual walked path. The model is capable of reproducing quantitatively relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of actual bumping events are also recovered.Furthermore, comparing with large datasets of real-life tracks involves an additional computational challenge so far neglected: identifying automatically, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. In order to tackle this challenge, we propose a novel and general approach based on a graph representation of pedestrian trajectories, which allows us to effectively operate complexity reduction for efficient data classification and selection.

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Section: Appendix A: Pedestrian Tracking At Eindhoven Train Station: mentioning

confidence: 99%

Physics-based modeling and data representation of pairwise interactions among pedestrians

Corbetta¹,

Meeusen²,

Lee³

et al. 2018

Phys. Rev. E

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“…1a). Technical aspects of our detection approach, inspired by [11], are discussed in the appendix of [6]. Furthermore, we employed the OpenPTV library [12], developed by the Particle Tracking Velocimetry [13] community in fluid mechanics, to perform heads tracking and to retrieve trajectories (cf.…”

Section: Measurement Sitementioning

confidence: 99%

Asymmetric Pedestrian Dynamics on a Staircase Landing from Continuous Measurements

Corbetta

Lee

Muntean

et al. 2016

Traffic and Granular Flow '15

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We investigate via extensive experimental data the dynamics of pedestrians walking in a corridor-shaped landing in a building at Eindhoven University of Technology. With year-long automatic measurements employing a Microsoft Kinect TM 3D-range sensor and ad hoc tracking techniques, we acquired few hundreds of thousands pedestrian trajectories in real-life conditions. Here we discuss the asymmetric features of the dynamics in the two walking directions with respect to the flights of stairs (i.e. ascending or descending). We provide a detailed analysis of position and speed fields for the cases of pedestrians walking alone undisturbed and for couple of pedestrians in counter-flow. Then, we show average walking velocities exploring all the observed combinations in terms of numbers of pedestrians and walking directions.

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“…For the sake of completeness we report here a primer of the measurement campaign and we refer interested readers to [1,12] for a more detailed overview of the traffic and to [16,36] for the techniques employed.…”

Section: Asymmetric Dynamics In a Staircase Landingmentioning

confidence: 99%

Frame vs. Trajectory Analyses of Pedestrian Dynamics Asymmetries in a Staircase Landing

Corbetta¹,

Lee²,

Muntean³

et al. 2017

Coll Dyn

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Real-life, out-of-laboratory, measurements of pedestrian walking dynamics allow extensive and fully-resolved statistical analyses. However, data acquisition in real-life is subjected to the randomness and heterogeneity that characterizes crowd flows over time. In a typical real-life location, disparate flow conditions follow one another in random order: for instance, a low density pedestrian co-flow dynamics may suddenly turn into a high density counter-flow scenario and then back again. Isolating occurrences of similar flow conditions within the acquired data is a paramount first step in the analyses in order to avoid spurious statistics and to enable qualitative comparisons.In this paper we extend our previous investigation on the asymmetric pedestrian dynamics on a staircase landing, where we collected a large statistical database of measurements from ad hoc continuous recordings [1]. This contribution has a two-fold aim: first, method-wise, we discuss an analysis workflow to consider large-scale experimental measurements, suggesting two querying approaches to automatically extract occurrences of similar flow scenarios out of datasets. These pursue aggregation of similar scenarios on either a frame or a trajectory basis. Second, we employ these two different perspectives to further explore asymmetries in the pedestrian dynamics in our measurement site. We report cross-comparisons of statistics of pedestrian positions, velocities and accelerations vs. flow conditions as well as vs. querying approach.

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Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method

Cited by 52 publications

References 35 publications

Physics-based modeling and data representation of pairwise interactions among pedestrians

Physics-based modeling and data representation of pairwise interactions among pedestrians

Asymmetric Pedestrian Dynamics on a Staircase Landing from Continuous Measurements

Frame vs. Trajectory Analyses of Pedestrian Dynamics Asymmetries in a Staircase Landing

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