Simulating crowd behaviour combining both microscopic and macroscopic rules

Saeed, R. A.; Recupero, Diego Reforgiato; Remagnino, Paolo

doi:10.1016/j.ins.2021.11.028

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…The proposed crowd simulator CrowdSim2 2 is the next version of crowd simulator CrowdSim [31] especially for testing multi-object tracking algorithms but also for action and object detection. It uses the microscopic (or 'agent-based') crowd simulation methods that model the behavior of each person, from which collective behavior can then emerge [32,20]. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents.…”

Section: Related Workmentioning

confidence: 99%

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Foszner

Szczęsna

Ciampi

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.

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Section: Related Workmentioning

confidence: 99%

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Foszner

Szczęsna

Ciampi

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

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“…The output of our model is the collision free trajectories of agents (i.e., locations in q time steps). Our model is the mapping of the input to the output, as shown in Equations ( 1) to (3).…”

Section: Our Modelmentioning

confidence: 99%

“…The movements of heterogeneous groups are very complex and depend on a large number of real data. 3 The cost of obtaining real data is high, which requires high precision equipment and heavy data annotation. 4 It is a challenge to use less data and depict movements of heterogeneous groups truly.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous group path planning algorithm based on data and mechanism model

Li,

2023

Computer Animation & Virtual

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It is a challenging task to find a feasible path from the start to the end for heterogeneous group and avoid collision between dynamic agents and static obstacles. The existing methods are usually applicable to static simple scenes or the type of scenes including a single kind of agents, and it is difficult to meet the high dynamic heterogeneous group movements. To address the above issues, we propose a hybrid driven heterogeneous group path planning method based on data and mechanism model. Data and mechanism model are combined to drive movements of heterogeneous groups. The experimental results show that our method can describe movements of heterogeneous groups more realistically and solve the collision avoidance of heterogeneous groups well. We quantitatively evaluate our method using metrics such as the number of inflection points and the average turning angle. Average turning angle has decreased by 59.50% on average over prior methods. Number of inflection points has decreased by 69.19% on average over prior methods.

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“…In [8], the authors proposed a simulation model of pedestrian behavior in different groups and crowd movement, creating individual trajectories for each person in the group. Other studies focus on evacuation planning [9], evaluating and optimizing evacuation plans [10][11][12][13], and studying human behavior in emergencies [14,15]. According to [16], the study constructs a BIM-FDS building fire simulation model to reveal the fire smoke dispersion law under the coupling of the typical building structure and fire protection systems.…”

Section: Introductionmentioning

confidence: 99%

Combining MAS-GiG Model and Related Problems to Optimization in Emergency Evacuation

Huyen

Occello

2023

Computers

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Emergency evacuation is of paramount importance in protecting human lives and property while enhancing the effectiveness and preparedness of organizations and management agencies in responding to emergencies. In this paper, we propose a method for evacuating passengers to safe places with the shortest possible evacuation time. The proposed method is based on a multi-level multi-agent MAS-GiG model combined with three problems. First, constructing a path map to select the shortest path; second, dividing the space of the experimental environment into smaller areas for efficient management, monitoring, and guiding evacuation; the third, adjusting the speed to handle collision issues and maintain distance between two or more groups of evacuees while moving. We extend our previous study by establishing groups based on the location of passengers and using a MAS-GiG model to guide evacuation. We compare the proposed method with our previous method to provide specific evaluations for the research and research in the future. We tested two methods in the departure hall, first floor, Danang International Airport, Vietnam.

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Simulating crowd behaviour combining both microscopic and macroscopic rules

Cited by 10 publications

References 39 publications

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Heterogeneous group path planning algorithm based on data and mechanism model

Combining MAS-GiG Model and Related Problems to Optimization in Emergency Evacuation

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