Simulating crowds based on a space colonization algorithm

Bicho, Alessandro de Lima; Rodrigues, Rafael Araújo; Musse, Soraia Raupp; Jung, Cláudio Rosito; Paravisi, Marcelo; Magalhães, Leo Pini

doi:10.1016/j.cag.2011.12.004

Cited by 52 publications

(19 citation statements)

References 14 publications

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“…As mentioned earlier, the Space D* is based on two algorithms that are discussed briefly below: the first is the D*, widely used for path-planning in unknown environments, and the Space Colonization algorithm, which was developed for modeling plant growth [26] and used for the simulation of crowd behaviors [1].…”

Section: Underlying Foundationsmentioning

confidence: 99%

“…In [1], the Space Colonization algorithm was adapted to simulate crowd behaviors. The concepts of vein nodes and auxins were transformed into agents and markers of free spaces.…”

Section: Space Colonization Algorithmmentioning

confidence: 99%

“…In [1], some modifications on the algorithm were also proposed. Firstly, the markers have become persistent, not being "consumed" by the agents, but instead, allocated when an agent comes up and released when it leaves.…”

Section: Space Colonization Algorithmmentioning

confidence: 99%

“…In the sixth stage, all markers contained in the environment are associated to their nearest robot, provided that they are within the reach of the robot. The difference from the simulation of crowd behaviors [1] is that, in the proposed method, this step is decentralized, as it is up to each robot to allocate all the markers located within its reach, considering only those that are closer to themselves than to other robots.…”

Section: End While Endmentioning

confidence: 99%

“…The current work proposes an extension of the D* algorithm for multi-robot environments, aiming to provide a collaborative path-planning approach for multi-robot world populated. The algorithm, called Space D*, combines the D* algorithm with the Space Colonization algorithm, originally designed to simulate the plant modeling [26] and later adapted for the simulation of crowd behaviors [1]. The main feature of the Space Colonization algorithm is the preference shift by spaces, to avoid the collision risk given the uncertainty and scalability of mobile and fixed obstacles.…”

Section: Introductionmentioning

confidence: 99%

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Space D*

et al. 2012

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This paper describes a new method of pathplanning for multiple robots in unknown environments. The method, called Space D*, is based on two algorithms: the D*, which is an incremental graph search algorithm, and the Space Colonization algorithm, previously used to simulate crowd behaviors. The path-planning is achieved through the exchange of information between the robots. So decentralized, each robot performs its path-planning, which provides an obstacle-free path with the least number of robots around. The major contribution of the proposed method is that it generates paths in spacious environments facilitating the control of robots and thus presenting itself in a viable way for using in areas populated with multiple robots. The results obtained validate the approach and show the advantages in comparison with using only the D* method.

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Section: Underlying Foundationsmentioning

confidence: 99%

“…In [1], the Space Colonization algorithm was adapted to simulate crowd behaviors. The concepts of vein nodes and auxins were transformed into agents and markers of free spaces.…”

Section: Space Colonization Algorithmmentioning

confidence: 99%

Section: Space Colonization Algorithmmentioning

confidence: 99%

Section: End While Endmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Space D*

et al. 2012

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Crowd Evacuation Management with Optimal Time‐Multiplexed Exit‐Choice Recommendations through Simulation Optimization

Lopez‐Carmona,

Paricio‐Garcia

2024

Advcd Theory and Sims

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Exit‐choice behavior significantly influences the performance of egress operations in crowded spaces. Thus, strategically issuing active exit‐choice recommendations that provide clear and concise guidance to pedestrians can help improve evacuation safety and efficiency. This study investigates how to optimally regulate pedestrian density at the exits by generating time‐multiplexed exit‐choice recommendations. The study develops a simulated crowd evacuation scenario with multiple exits, where pedestrians receive periodic sequences of exit‐choice instructions. The optimal period for each instruction is obtained through simulation‐optimization processes with three minimization objectives: evacuation time, cumulative density, and cumulative density variance. First, the effect of collective behaviors on evacuation performance is studied, which justifies the need for active mechanisms to improve exit‐choice decision‐making coordination. Then, several optimal plans for the different objectives are obtained and evaluated regarding evacuation time and cumulative density performance. Finally, the influence of adoption rate and collective commitment to instructions are evaluated. This study suggests that static plans, defined as time‐multiplexed exit‐choice instructions obtained through simulation‐optimization, reasonably represent a first step in the real deployment of intelligent crowd management systems.

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Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

Favaretto

Santos²,

Balotin³

et al. 2019

Computer Animation & Virtual

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This paper presents a study, organized in two phases, regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures—personal spaces. First, we want to study and compare the spatial behavior different populations adopt with respect to their personal space. Second, we want to use simulation of virtual agents to artificially generate movements of people in similar situations and validate them using real video sequences. Our main goal is to be able to extract from video sequences and then simulate variations in populations in a coherent way with literature that studies cultural aspects. In addition to the cultural aspects, we also investigate the personality model in the studied videos using OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Finally, we propose a way to simulate the fundamental diagram experiment from other countries using the OCEAN psychological trait model as input. Results indicate that the simulated countries have consistent characteristics with the expected literature.

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Simulating crowds based on a space colonization algorithm

Cited by 52 publications

References 14 publications

Space D*

Space D*

Crowd Evacuation Management with Optimal Time‐Multiplexed Exit‐Choice Recommendations through Simulation Optimization

Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

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