Enhancing emergency pedestrian safety through flow rate design: Bayesian-Nash Equilibrium in multi-agent system

Liao, Can; Guo, Hao; Zhu, Kunpeng; Shang, Jennifer

doi:10.1016/j.cie.2019.106058

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…In order to construct the cellular automata for government–industry–university–institute collaborative innovation in green technology to simulate green technology innovation behavior diffusion in the region, the basic assumptions and payoff matrices in Section 3.2.1 are applied in this section, and its basic components (cell (

), cell space (

), neighbour (

), cell state (

) and evolution rule (

)) are clarified [ 52 , 53 , 54 ]. The specific settings are as follows:…”

Section: Modelmentioning

confidence: 99%

Research on the Mechanism of Government–Industry–University–Institute Collaborative Innovation in Green Technology Based on Game–Based Cellular Automata

Zhou

2022

IJERPH

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In order to ensure stable cooperation among the government, enterprise and university/institute in the green technology innovation process and guide an increasing number of innovation agents in the region to adopt cooperation, this paper studies the mechanism of green technology innovation. A tripartite evolutionary game model is established and the strategy choices of the government, industry and university/institute are analyzed through mathematical derivation. On this basis, the cellular automata theory is used to explore strategy choices of all innovation agents in the region from the perspective of a spatial game. From the numerical tests, the following results are obtained: increasing the cooperative innovation revenue, fairness of this revenue distribution or penalties for breach of contract can consolidate the cooperative relationship among the government, enterprise and university/institute, achieving the goal of guiding all innovation agents in the region to accept the collaborative innovation mode; regulating the government subsidy or government penalty can consolidate the cooperative relationship among participants in the pilot project, but cannot guide all innovation agents in the region accept the collaboration innovation mode. This paper’s results not only enrich the theory of government–industry–university–institute collaborative innovation in green technology, but provide ideas for stable cooperation mechanisms and comprehensive promotion of this collaborative innovation mode as well.

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), cell space (

), neighbour (

), cell state (

) and evolution rule (

)) are clarified [ 52 , 53 , 54 ]. The specific settings are as follows:…”

Section: Modelmentioning

confidence: 99%

Research on the Mechanism of Government–Industry–University–Institute Collaborative Innovation in Green Technology Based on Game–Based Cellular Automata

Zhou

2022

IJERPH

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“…Lopez et al conducted the simulations using the I2Sim simulation tool (Marti et al, 2008) and the results' performance was quite higher compared to their previous studies. Liao et al proposed a methodology for multi-agent simulation systems (Liao et al, 2019), that exploited the Bayesian-Nash Equilibrium (Kajii & Morris, 1997) for the decision-making process. The method was calibrated and validated using data collected from real experiments.…”

Section: Agent Behaviormentioning

confidence: 99%

Crowd simulation for crisis management: The outcomes of the last decade

Sidiropoulos

Kiourt

Moussiades

2020

Machine Learning with Applications

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“…ere is a gap (11,13) on the west side of the isolation bar, which is the bottleneck of the pedestrian flow [32]. An agent travels from the (2, 2) to the blue cell (24,8). During walking, gap (10,13) is temporarily occupied by other agents.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Existing studies only analyze the speed characteristics of pedestrian flow in the multiagent system [24][25][26][27] or study the large-scale macrocollection and distribution capacity of pedestrians [28][29][30]. Few research studies focus on the mechanism of path planning for the multiagent at mesolevel and consider different factors such as gender and luggage.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

Liu

Guo

et al. 2020

Journal of Advanced Transportation

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Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.

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Enhancing emergency pedestrian safety through flow rate design: Bayesian-Nash Equilibrium in multi-agent system

Cited by 12 publications

References 24 publications

Research on the Mechanism of Government–Industry–University–Institute Collaborative Innovation in Green Technology Based on Game–Based Cellular Automata

Research on the Mechanism of Government–Industry–University–Institute Collaborative Innovation in Green Technology Based on Game–Based Cellular Automata

Crowd simulation for crisis management: The outcomes of the last decade

A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

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