Self-propelled pedestrian dynamics model: Application to passenger movement and infection propagation in airplanes

Namilae, Sirish; Srinivasan, Ashok; Mubayi, Anuj; Scotch, Matthew; Pahle, Robert

doi:10.1016/j.physa.2016.08.028

Cited by 38 publications

(48 citation statements)

References 42 publications

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“…Social force based pedestrian models have been validated in numerous studies [50,51]. The model described here has been shown to reproduce pedestrian evacuation data from airplanes as well as reproduce the experimentally verified speed-density diagrams [31,52].…”

Section: Model Application To Pedestrian Queue Configurationsmentioning

confidence: 84%

“…, results in a self-propulsion force that is balanced by a repulsion force � f ij ðtÞ to obstacles in the direction of motion. In this study, we use the Lennard-Jones type repulsion term used earlier by Namilae et al [30,31]. While Eq (1) describes the general motion of pedestrians, we need to introduce modifications to this equation to account for slow-moving pedestrian queues.…”

Section: Pedestrian Dynamicsmentioning

confidence: 99%

“…Since its conception, there have been several advances in social force models involving force field estimations [24], algorithmic developments [25,26] and applications in situations like panic [27], traffic dynamics [28] and evacuation [29]. Namilae et al [30,31] have used pedestrian dynamics described by the social force model in a multiscale model to study the spread of epidemics during air travel.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread

Derjany¹,

Namilae²,

Liu³

et al. 2020

PLoS ONE

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There is direct evidence for the spread of infectious diseases such as influenza, SARS, measles, and norovirus in locations where large groups of people gather at high densities e.g. theme parks, airports, etc. The mixing of susceptible and infectious individuals in these high people density man-made environments involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. We address this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. The pedestrian dynamics model is utilized to generate the trajectories of motion and contacts between infected and susceptible individuals. We incorporate this information into a stochastic infection dynamics model with infection probability and contact radius as primary inputs. This generic model is applicable for several directly transmitted diseases by varying the input parameters related to infectivity and transmission mechanisms. Through this multiscale framework, we estimate the aggregate numbers and probabilities of newly infected people for different winding queue configurations. We find that the queue configuration has a significant impact on disease spread for a range of infection radii and transmission probabilities. We quantify the effectiveness of wall separators in suppressing the disease spread compared to rope separators. Further, we find that configurations with short aisles lower the infection spread when rope separators are used.

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Section: Model Application To Pedestrian Queue Configurationsmentioning

confidence: 84%

Section: Pedestrian Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread

Derjany¹,

Namilae²,

Liu³

et al. 2020

PLoS ONE

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“…The Self-Propelled Entity Dynamics (SPED) model is used to simulate movement of passengers in airplanes [4], [5]. Its basic computational structure is similar to Molecular Dynamics, with each passenger and fixed surface treated as a point particle.…”

Section: A Modeling Passenger Movement In Planesmentioning

confidence: 99%

Parallel Low Discrepancy Parameter Sweep for Public Health Policy

Chunduri¹,

Ghaffari²,

Lahijani³

et al. 2018

EasyChair Preprints

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Abstract-Numerical simulations are used to analyze the effectiveness of alternate public policy choices in limiting the spread of infections. In practice, it is usually not feasible to predict their precise impacts due to inherent uncertainties, especially at the early stages of an epidemic. One option is to parameterize the sources of uncertainty and carry out a parameter sweep to identify their robustness under a variety of possible scenarios. The Self Propelled Entity Dynamics (SPED) model has used this approach successfully to analyze the robustness of different airline boarding and deplaning procedures. However, the time taken by this approach is too large to answer questions raised during the course of a decision meeting. In this paper, we use a modified approach that pre-computes simulations of passenger movement, performing only the disease-specific analysis in real time. A novel contribution of this paper lies in using a low discrepancy sequence (LDS) in the parameter sweep, and demonstrating that it can lead to a reduction in analysis time by one to three orders of magnitude over the conventional latticebased parameter sweep. However, its parallelization suffers from greater load imbalance than the conventional approach. We examine this and relate it to number-theoretic properties of the LDS. We then propose solutions to this problem. Our approach and analysis are applicable to other parameter sweep problems too. The primary contributions of this paper lie in the new approach of low discrepancy parameter sweep and in exploring solutions to challenges in its parallelization, evaluated in the context of an important public health application.

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“…We have formulated a multiphysics computational model [5,6] that incorporates particle dynamics based pedestrian movement of travelers in transit hubs (e.g. airports), contact analysis and stochastic infectious disease spread, to frame and analyze transportation policies that can mitigate infectious disease spread.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

et al. 2020

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In this paper, we present a formulation for a multiscale model combining a social force based pedestrian movement including collision avoidance and a stochastic infection dynamics framework to evaluate the spread of multiple infectious diseases during air travel. We apply the multiscale model to evaluate pedestrian movement strategies that can reduce infection spread during air travel. The results are presented for airport lounge and airplane boarding and deplaning. Use of parallel computing to evaluate the vast parameter space created due to stochasticity and discretionary pedestrian behavior is addressed.

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Self-propelled pedestrian dynamics model: Application to passenger movement and infection propagation in airplanes

Cited by 38 publications

References 42 publications

Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread

Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread

Parallel Low Discrepancy Parameter Sweep for Public Health Policy

Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

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