Multi-Level Multi-Stage Agent-Based Decision Support System for Simulation of Crowd Dynamics

Alqurashi, Raghda; Altman, Tom

doi:10.1109/iceccs2018.2018.00017

Cited by 3 publications

(7 citation statements)

References 35 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%

“…In the MLMS-ABM simulation model, it is assumed that the maximum speed of vehicles is set to be s = 65 mph, that is, about 3 cells/time-stamp in the CA environment. More details about the agents' movement characteristics are addressed in detail in [12].…”

Section: Agent Configurationmentioning

confidence: 99%

“…At every time-stamp and for each agent, a new target cell is selected from the five possible candidate cells in the Moore neighborhood [43] to define the travelling trajectory, as represented in Figure 4. This choice is based on the probabilistic calculation performed on each candidate cell, and the cell with the highest probability is selected [12]. Applying the A * algorithm [44] only as a routing technique does not specify where the processing for the routes exists nor does it capture the varying conditions of congestions at any point in time.…”

Section: Transition Decision-making Modelmentioning

confidence: 99%

“…Additionally, this sub-model helps each agent to check for possible interactions with other agents. A more detailed analysis of this sub-model is carried out in [12].…”

Section: Transition Decision-making Modelmentioning

confidence: 99%

“…One particularly effective computational simulation method is an agent-based model (ABM) [11][12][13][14][15][16][17], which considers the complex system as a decentralized, multi-agent system (MAS) [11], where each agent can communicate only with its immediate neighboring agents. The ABM approach is a form of optimization of individual solutions, and it applies to systems of interacting, autonomous, and individual agents.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Agent Configurationmentioning

confidence: 99%

Section: Transition Decision-making Modelmentioning

confidence: 99%

“…Additionally, this sub-model helps each agent to check for possible interactions with other agents. A more detailed analysis of this sub-model is carried out in [12].…”

Section: Transition Decision-making Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Agent-Based Modeling for Improved Traffic Routing

Alqurashi

Altman

2019

Applied Sciences

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Definition of Agent Goals by Means of Missions

Varga

Adamko

2019

2019 International Conference on Information and Digital Technologies (IDT)

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Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling

Zhong

2020

ACM Trans. Model. Comput. Simul.

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One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules. Furthermore, the proposed approach considers both static and dynamic features during modeling to generate a realistic crowd, based on the assumption that humans tend to consider different features with respect to their states. In the proposed method, the automatic behavior rule generation problem is formulated as a symbolic regression problem. Then, the problem is solved by multi-objective genetic programming. On one hand, to improve the interpretability of the behavior rules found, a new mechanism is proposed to guide the algorithm to find concise and dimensionally consistent solutions. On the other hand, decisions made by considering static and dynamic features respectively are combined to improve the generated crowd realism. To validate the effectiveness of the proposed method, three real-world datasets are utilized for training and testing. The simulation results demonstrate that the proposed method is able to find universal behavior rules that are competitive to previous work in accuracy while having better interpretability.

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Multi-Level Multi-Stage Agent-Based Decision Support System for Simulation of Crowd Dynamics

Cited by 3 publications

References 35 publications

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Definition of Agent Goals by Means of Missions

Dimensionally Aware Multi-Objective Genetic Programming for Automatic Crowd Behavior Modeling

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