research-article

The General Model of Atom-type Simulation Members in Crowd Network

Authors:

Zhaoyu Han,

Hongbo Sun,

Baode Fan,

Yongguang ZhaoAuthors Info & Claims

ICCSE'19: Proceedings of the 4th International Conference on Crowd Science and Engineering

Pages 161 - 167

https://doi.org/10.1145/3371238.3371263

Published: 18 October 2019 Publication History

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Abstract

Crowd network is a deep integration system, which includes information, consciousness and physics. Those system are characterized by large amount of data, openness, self-organization and ecologization. In terms of its components, the digital itself has taken on great changes. There have been heterogeneity changes in the way of interconnection, and the interconnection among digital-selves has been further deepened in depth and breadth. Modeling and simulation of crowd behavior and intelligence is widely used in many fields and disciplines. This paper proposes a general model of simulation members for digital-self in crowd networks, which is used to modelling the intelligent activities of digital-selves. The general model can be separated into eight parts. These eiht parts are pattern, executor, decider, decompositor, interator, affector, monitor and comparator.

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Cited By

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Sun HRen YZhao HMa GDuan YLiu LWang ZLi LXing A(2023)A fixed point analysis of multiple information coevolution spreading on social networksInformation Sciences10.1016/j.ins.2023.118974638(118974)Online publication date: Aug-2023
https://doi.org/10.1016/j.ins.2023.118974
Saglietto L(2021)Bibliometric analysis of sharing economy logistics and crowd logisticsInternational Journal of Crowd Science10.1108/IJCS-07-2020-00145:1(31-54)Online publication date: 22-Mar-2021
https://doi.org/10.1108/IJCS-07-2020-0014

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ICCSE'19: Proceedings of the 4th International Conference on Crowd Science and Engineering

October 2019

246 pages

ISBN:9781450376402

DOI:10.1145/3371238

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 18 October 2019

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National Key R&D Program of China

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ICCSE'19

ICCSE'19: The 4th International Conference on Crowd Science and Engineering

October 18 - 21, 2019

Jinan, China

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ICCSE'19 Paper Acceptance Rate 35 of 92 submissions, 38%;

Overall Acceptance Rate 92 of 247 submissions, 37%

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Sun HRen YZhao HMa GDuan YLiu LWang ZLi LXing A(2023)A fixed point analysis of multiple information coevolution spreading on social networksInformation Sciences10.1016/j.ins.2023.118974638(118974)Online publication date: Aug-2023
https://doi.org/10.1016/j.ins.2023.118974
Saglietto L(2021)Bibliometric analysis of sharing economy logistics and crowd logisticsInternational Journal of Crowd Science10.1108/IJCS-07-2020-00145:1(31-54)Online publication date: 22-Mar-2021
https://doi.org/10.1108/IJCS-07-2020-0014

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A General Simulation Framework for Crowd Network Simulations

Small scale crowd behavior classification by Euclidean distance variation-weighted network

The hierarchical behavior model for crowd simulation

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