File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Bayesian inference approach to the development of a multidirectional pedestrian stream model

TitleA Bayesian inference approach to the development of a multidirectional pedestrian stream model
Authors
Issue Date2015
PublisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21
Citation
Transportmetrica A: Transport Science, 2015, v. 11 n. 1, p. 61-73 How to Cite?
AbstractIn this paper, we develop a mathematical model to represent the conflicting effects of multidirectional pedestrian flows in a large crowd. The model is formulated based on Drake's model of traffic flow. Rather than relate the speed of a pedestrian stream solely to the pedestrian density, we introduce the flow ratio and intersecting angle between streams as variables. To calibrate the model, data collection was conducted through the video recording of pedestrian movements on a pedestrian street in Mong Kok, Hong Kong. Bayesian inference was adopted to calibrate the parameters based on the information from a previous experiment. Finally, we study the relationships among the speed, density, flow and intersecting angles of the pedestrian streams and predict how these variables affect the pedestrian movements.
Persistent Identifierhttp://hdl.handle.net/10722/207716
ISSN
2015 Impact Factor: 1.477
2015 SCImago Journal Rankings: 1.352

 

DC FieldValueLanguage
dc.contributor.authorXie, S-
dc.contributor.authorWong, SC-
dc.date.accessioned2015-01-19T09:18:31Z-
dc.date.available2015-01-19T09:18:31Z-
dc.date.issued2015-
dc.identifier.citationTransportmetrica A: Transport Science, 2015, v. 11 n. 1, p. 61-73-
dc.identifier.issn2324-9935-
dc.identifier.urihttp://hdl.handle.net/10722/207716-
dc.description.abstractIn this paper, we develop a mathematical model to represent the conflicting effects of multidirectional pedestrian flows in a large crowd. The model is formulated based on Drake's model of traffic flow. Rather than relate the speed of a pedestrian stream solely to the pedestrian density, we introduce the flow ratio and intersecting angle between streams as variables. To calibrate the model, data collection was conducted through the video recording of pedestrian movements on a pedestrian street in Mong Kok, Hong Kong. Bayesian inference was adopted to calibrate the parameters based on the information from a previous experiment. Finally, we study the relationships among the speed, density, flow and intersecting angles of the pedestrian streams and predict how these variables affect the pedestrian movements.-
dc.languageeng-
dc.publisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21-
dc.relation.ispartofTransportmetrica A: Transport Science-
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica A: Transport Science on 16 Jun 2014, available online: http://www.tandfonline.com/doi/abs/10.1080/23249935.2014.924165-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA Bayesian inference approach to the development of a multidirectional pedestrian stream model-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturepostprint-
dc.identifier.doi10.1080/23249935.2014.924165-
dc.identifier.hkuros242202-
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.spage61-
dc.identifier.epage73-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats