Multi-class Agent-Based Model of Crowd Dynamics

Alqurashi, Raghda; Altman, Tom

doi:10.1109/csci.2017.317

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The proposed model, based on the ABM and non-homogeneous CA approaches researched in [12,41], has been exploited to provide a multi-layered decision support system in cases of accident congestions, and it develops a new simulation method to understand the movement of vehicular traffic in case of accidents and/or congestion.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Alqurashi

Altman

2019

Applied Sciences

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Agent-based model (ABM) simulation is a bottom-up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers with real-time alternative routes, computed via a decentralized multi-agent model, that tries to achieve a system-optimal traffic distribution within an entire system, thus reducing the total travel time of all the drivers. The presented work explores a decentralized ABM technique on an autonomous microgrid that is represented through cellular automata (CA). The proposed model was applied to high-density traffic congestion events such as car accidents or lane closures, and its effectiveness was analyzed. The experimental results confirm the efficiency of the proposed model in not only accurately simulating the driver behaviors and improving vehicular traffic flows during congestion but also by suggesting changes to traffic dynamics during the simulations, such as avoiding obstacles and high-density areas and then selecting the best alternative routes. The simulation results validate the ability of the proposed model and the included decision-making sub-models to both predict and improve the behaviors and intended actions of the agents.

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Section: Methodsmentioning

confidence: 99%

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Alqurashi

Altman

2019

Applied Sciences

Self Cite

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“…The dynamics of a crowd [11] cannot simply be limited to the principles of mechanics and deterministic causality [12]. In fact, the heterogeneous behaviors of individuals and their social dynamics can have an important influence on the crowd dynamics and, in particular, on the strategy they use to reach a certain goal.…”

Section: Introductionmentioning

confidence: 99%

Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems

Berceanu

Banu

Husebø

et al. 2023

IEEE Systems Journal

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As a complex system, crowd dynamics emerge bottom-up from the local interactions between pedestrians as component subsystems. This article proposes a predictive agent-based crowd simulation model to analyze the outcomes of emergency evacuation scenarios taking into account collisions between pedestrians, smoke, fire sprinklers, and exit indicators. The crowd model is based on a decentralized control system structure, where each pedestrian agent is governed through a deliberative-reactive control architecture. The simulation model for evacuation includes a routing-based control system for dynamic-guided evacuation. A design case illustrates the modeling process. Results show that the crowd simulation model based on agent autonomy and local interactions is able to generate higher level crowd dynamics through emergence.

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Reinforcement Learning for Crowd Control Optimization with An A* Based Social Force Interaction Model

Liang,

Wei,

et al. 2023

2023 13th International Conference on Information Science and Technology (ICIST)

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Multi-class Agent-Based Model of Crowd Dynamics

Cited by 3 publications

References 6 publications

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems

Reinforcement Learning for Crowd Control Optimization with An A* Based Social Force Interaction Model

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