Counterflow model for agent-based simulation of crowd dynamics

Heliövaara, Simo; Korhonen, Timo; Hostikka, Simo; Ehtamo, Harri

doi:10.1016/j.buildenv.2011.08.020

Cited by 111 publications

(60 citation statements)

References 14 publications

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“…Zhao et al present a simulation of pedestrians' backtrack and the waiting behavior [10]. Heliovaara et al present a model for agents' behavior in counterflow situations [11]. Pouke et al calibrate fluctuations of pedestrian traffic in a random model [12].…”

Section: A Interaction Simulationmentioning

confidence: 99%

Multi-Character Fighting Simulation

Sukoco¹,

Wardoyo²,

Harjoko³

et al. 2018

IJISA

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Abstract-In the development of and research into multicharacter fighting computer games, Non-Player Characters (NPCs) frequently seem less intelligent owing to them having a single focus. As such, multi-character fighting becomes one-on-one fighting; one character will encounter another character only once the previous opponent is defeated. This study develops a new model in multi-character fighting, in which each NPC can simultaneously fight against many characters. Following this model, each character becomes an agent that makes his own decisions. The first advantage of this model is the integration of multi-character behaviors in fights. Each character can seek out enemies/opponents, select one target opponent, avoid obstacles, approach the target opponent, change the target opponent, and then defeat the opponent or be defeated by the opponent; in other words, each character can thus fight against many opponents. All of the behaviors in the fight take place automatically. The second advantage of this model is that each character does not only focus on the opponent being targeted, but also on the other opponents surrounding him. Each character can move from one opponent to another, even when the target opponent is not yet defeated. The third advantage of this model is that each character can move to another fight cluster, thus ensuring that fights seem more dynamic. This research has experimented with the model using a 3D application that can run on personal computers or smart phones.

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Section: A Interaction Simulationmentioning

confidence: 99%

Multi-Character Fighting Simulation

Sukoco¹,

Wardoyo²,

Harjoko³

et al. 2018

IJISA

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“…(4)- (12) describe the displacement of the agents. The rotation of an agent is treated analogously (13) where φ i (t)is the agent's azimuthal angle, is the angular velocity, and is the desired angular velocity. Time τ i is the characteristic one over which the agent alters its current angular velocity to the desired one given by (14) where is the actual facing direction of agent i, is the desired facing direction, and There are various parameters in the agent-movement model.…”

Section: Crowd-movement Modelmentioning

confidence: 99%

“…These forces are determined by solving Newton's equations of motion, which can be extended to include various types of behaviors such as overtaking [12], counterflow collision avoidance [13], and information exchange [14]. However, the need for complicated expressions (which are relatively hard to calibrate) to calculate these forces imposes a heavy computational burden.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Evacuation in Passenger Ships Using a Goal-Driven Decision-Making Model

Li²,

2017

Polish Maritime Research

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A new agent-based model is proposed to support designers in assessing the evacuation capabilities of passenger ships and in improving ship safety. It comprises models for goal-driven decision-making, path planning, and movement. The goal-driven decision-making model determines an agent's target by decomposing abstract goals into subgoals. The path-planning model plans the shortest path from the agent's current position to its target. The movement model is a combination of social-force and steering models to control the agent in moving along its path. The utility of the proposed model is verified using 11 tests for passenger ships proposed by the Maritime Safety Committee of the International Maritime Organization.

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“…Collision avoidance behaviors were adopted to SFM by adding either a component to the desired velocity (Smith et al, 2009) or a new force to change the pedestrian trajectories (Pelechano et al, 2007). Heliövaara et al (2012) presented a new combined model based on SFM, which alters the desired moving direction of the pedestrian and takes into account both collision avoidance and lane formation. Guo (2014) considered the influence of position factors (between the pedestrian and the entrance/exit boundary) on the counter flow and the lane formation.…”

Section: Introductionmentioning

confidence: 99%

Simulation model of self-organizing pedestrian movement considering following behavior

Yuan

Jia

Liao

et al. 2017

Frontiers Inf Technol Electronic Eng

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A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.

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Counterflow model for agent-based simulation of crowd dynamics

Cited by 111 publications

References 14 publications

Multi-Character Fighting Simulation

Multi-Character Fighting Simulation

Agent-Based Evacuation in Passenger Ships Using a Goal-Driven Decision-Making Model

Simulation model of self-organizing pedestrian movement considering following behavior

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