Generating Pedestrian Trajectories Consistent with the Fundamental Diagram Based on Physiological and Psychological Factors

Narang, Sahil; Best, Andrew; Curtis, Sean; Manocha, Dinesh

doi:10.1371/journal.pone.0117856

Cited by 34 publications

(35 citation statements)

References 33 publications

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“…Furthermore, it is used for modeling [Narang et al 2015] and evaluating models [Helbing and Molnár 1995;Fang et al 2012] and is a cornerstone test to decide whether a model generates pedestrian streams with fidelity Steiner et al 2007]. Predtechenskii and Milinskii [1978] show the descriptive capability of the fundamental diagram on different scenarios (horizontal paths, stairs and openings) and under different circumstances (emergency, normal and comfortable conditions), and demonstrate that the averaged speed of the pedestrians' flow is not only a function of the density but also of the type of path.…”

Section: Characterization Of Pedestrian Dynamicsmentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this paper is to provide a bibliographic outlook so that the reader could have a quick access to the most relevant works related with this problem. We have used three main axes to organise the paper contents: pedestrian models, validation techniques and multiscale approaches. The backbone of the paper is the classification of existing pedestrian models; we have organised the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then, the main existing validation methods, oriented to evaluate the behavioural quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we firstly focus on the behavioural scale (combinations of micro and macro pedestrian models) and secondly, on the scale size (from individuals to crowds). The paper begins introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can do in a near future to this exciting area.

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Section: Characterization Of Pedestrian Dynamicsmentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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“…Best et al . [BNCM14, NBCM15] proposed a DenseSense (DS) model which uses a density‐dependent filter to correct the preferred velocity from global planner. Zhao et al .…”

Section: Related Workmentioning

confidence: 99%

“…In terms of behaviours being modelled, we propose a novel gap‐seeking behaviour model to capture how a person utilizes free spaces in surrounding area for steering. While the DS model [BNCM14, NBCM15] has similar objective, we introduce the notion of gap to represent free spaces that are dynamically formed due to the movement of crowds. Thus, instead of evaluating crowd density at different points as in DS, the agents in our model can directly select a suitable gap to move to, which we believe can better imitate the behaviours of a real person.…”

Section: Related Workmentioning

confidence: 99%

“… (a) Fundamental diagrams evaluation The fundamental diagram is commonly used to evaluate pedestrian dynamics, which characterizes the observed relationship between pedestrian speed and density (usually speed decreases as density increases). The procedure to construct the fundamental diagram is similar to the one adopted in the work for the DS model [NBCM15]. For each scenario, we first define a rectangular region where each agent must pass through.…”

Section: Case Studiesmentioning

confidence: 99%

“…Bruneau and Pettré [BP15] introduced a mid-term planning model which also explores future agent interactions within the next n time steps and devises optimal avoidance strategy based on an energy cost function. Best et al [BNCM14,NBCM15] proposed a DenseSense (DS) model which uses a density-dependent filter to correct the preferred velocity from global planner. Zhao et al [ZZC16] proposed a role-dependent (RD) steering model which identifies two important roles (i.e.…”

Section: Related Workmentioning

confidence: 99%

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ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

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Chai

et al. 2017

Computer Graphics Forum

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How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.

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On density–flow relationships during crowd evacuation

Haworth

Usman

Berseth

et al. 2017

Computer Animation & Virtual

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Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer-assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.

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Generating Pedestrian Trajectories Consistent with the Fundamental Diagram Based on Physiological and Psychological Factors

Cited by 34 publications

References 33 publications

Modeling, Evaluation, and Scale on Artificial Pedestrians

Modeling, Evaluation, and Scale on Artificial Pedestrians

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

On density–flow relationships during crowd evacuation

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