Predicting Evacuation Capacity for Public Buildings

Pejman Kamkarian; Henry Hexmoor

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Predicting Evacuation Capacity for Public Buildings

Topics: Collective Intelligence; Knowledge Representation and Reasoning

In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 433-440, 2013 , Barcelona, Spain

Authors: Pejman Kamkarian and Henry Hexmoor

Affiliation: Southern Illinois University, United States

Keyword(s): Networks of Bayesian Belief Revision, Public Space Safety, Crowd Evacuation.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Collective Intelligence ; Knowledge Representation and Reasoning ; Symbolic Systems

Abstract: This paper demonstrates a solution for analyzing public space evacuation rates. Evacuating from a public building in a reasonable amount of time is reliant upon how safe the space is in terms of achieving a minimum time to move people outside. In order to increase the safety of evacuation in public spaces, we employed the Bayesian Belief Network method. To have a better estimation pattern, we have to focus on important physical environmental features as well as crowd formation and specifications in a public space.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 18.191.202.45

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Kamkarian, P. and Hexmoor, H. (2013). Predicting Evacuation Capacity for Public Buildings. In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8565-39-6; ISSN 2184-433X, SciTePress, pages 433-440. DOI: 10.5220/0004217704330440

@conference{icaart13,
author={Pejman Kamkarian. and Henry Hexmoor.},
title={Predicting Evacuation Capacity for Public Buildings},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2013},
pages={433-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004217704330440},
isbn={978-989-8565-39-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Predicting Evacuation Capacity for Public Buildings
SN - 978-989-8565-39-6
IS - 2184-433X
AU - Kamkarian, P.
AU - Hexmoor, H.
PY - 2013
SP - 433
EP - 440
DO - 10.5220/0004217704330440
PB - SciTePress