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- Publisher Website: 10.1080/23249935.2014.924165
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Article: A Bayesian inference approach to the development of a multidirectional pedestrian stream model
Title | A Bayesian inference approach to the development of a multidirectional pedestrian stream model |
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Authors | |
Keywords | Bayesian inference empirical studies measurement multidirectional flow pedestrian |
Issue Date | 2015 |
Publisher | Taylor & 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/207716 |
ISSN | 2023 Impact Factor: 3.6 2023 SCImago Journal Rankings: 1.099 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xie, S | - |
dc.contributor.author | Wong, SC | - |
dc.date.accessioned | 2015-01-19T09:18:31Z | - |
dc.date.available | 2015-01-19T09:18:31Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Transportmetrica A: Transport Science, 2015, v. 11 n. 1, p. 61-73 | - |
dc.identifier.issn | 2324-9935 | - |
dc.identifier.uri | http://hdl.handle.net/10722/207716 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | Taylor & Francis. The Journal's web site is located at http://www.tandfonline.com/loi/ttra21 | - |
dc.relation.ispartof | Transportmetrica A: Transport Science | - |
dc.rights | This 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.subject | Bayesian inference | - |
dc.subject | empirical studies | - |
dc.subject | measurement | - |
dc.subject | multidirectional flow | - |
dc.subject | pedestrian | - |
dc.title | A Bayesian inference approach to the development of a multidirectional pedestrian stream model | - |
dc.type | Article | - |
dc.identifier.email | Wong, SC: hhecwsc@hku.hk | - |
dc.identifier.authority | Wong, SC=rp00191 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1080/23249935.2014.924165 | - |
dc.identifier.scopus | eid_2-s2.0-84919834306 | - |
dc.identifier.hkuros | 242202 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 61 | - |
dc.identifier.epage | 73 | - |
dc.identifier.isi | WOS:000346582900003 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2324-9935 | - |