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Title:

Operational Walking Dynamics of Crowds Modeled with Linear Regression

Accession Number:

01629160

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/isbn/9780309442114

Abstract:

In this study the influence of interaction characteristics on operational walking dynamics within a crowd—specifically the influence of the distance headway, time headway, angle of sight, angle of interaction, walking speed, and number of pedestrians located nearby on a pedestrian’s change in speed and direction—is investigated. To the authors’ best knowledge, this is the first time that the combined effect of the characteristics of interactions between pedestrians on the operational walking dynamics of pedestrians has been quantified. The walking speed and the number of pedestrians in close proximity were found to influence the adaptation of speed and direction. The other characteristics of the interaction affect either the change in speed (i.e., distance headway and interaction angle) or the change in direction (i.e., time headway and angle of sight). The results of this study strongly indicate that the density experienced by pedestrians is not the only characteristic of the crowd that affects pedestrians’ operational walking dynamics. Consequently, to model crowd movements correctly, the other characteristics of the interaction must also be taken into account in pedestrian flow theory and simulation models.

Monograph Accession #:

01656418

Report/Paper Numbers:

17-03335

Language:

English

Authors:

Duives, Dorine C
Daamen, Winnie
Hoogendoorn, Serge

Pagination:

pp 90–97

Publication Date:

2017

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2623
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309442114

Media Type:

Digital/other

Features:

Figures (2) ; References (25) ; Tables (5)

Subject Areas:

Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:15AM

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